Overview

Dataset statistics

Number of variables39
Number of observations626822
Missing cells6595079
Missing cells (%)27.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory186.5 MiB
Average record size in memory312.0 B

Variable types

Numeric14
Categorical24
Unsupported1

Alerts

FPA_ID has a high cardinality: 626822 distinct values High cardinality
NWCG_REPORTING_UNIT_ID has a high cardinality: 1405 distinct values High cardinality
NWCG_REPORTING_UNIT_NAME has a high cardinality: 1401 distinct values High cardinality
SOURCE_REPORTING_UNIT has a high cardinality: 4262 distinct values High cardinality
SOURCE_REPORTING_UNIT_NAME has a high cardinality: 3737 distinct values High cardinality
LOCAL_FIRE_REPORT_ID has a high cardinality: 6189 distinct values High cardinality
LOCAL_INCIDENT_ID has a high cardinality: 213528 distinct values High cardinality
FIRE_CODE has a high cardinality: 65982 distinct values High cardinality
FIRE_NAME has a high cardinality: 194780 distinct values High cardinality
ICS_209_INCIDENT_NUMBER has a high cardinality: 8000 distinct values High cardinality
ICS_209_NAME has a high cardinality: 7463 distinct values High cardinality
MTBS_ID has a high cardinality: 3602 distinct values High cardinality
MTBS_FIRE_NAME has a high cardinality: 3075 distinct values High cardinality
COMPLEX_NAME has a high cardinality: 824 distinct values High cardinality
CONT_TIME has a high cardinality: 1441 distinct values High cardinality
STATE has a high cardinality: 52 distinct values High cardinality
COUNTY has a high cardinality: 3090 distinct values High cardinality
FIPS_NAME has a high cardinality: 1658 distinct values High cardinality
OBJECTID is highly correlated with FOD_ID and 12 other fieldsHigh correlation
FOD_ID is highly correlated with OBJECTID and 5 other fieldsHigh correlation
FIRE_YEAR is highly correlated with OBJECTID and 4 other fieldsHigh correlation
DISCOVERY_DATE is highly correlated with OBJECTID and 4 other fieldsHigh correlation
DISCOVERY_DOY is highly correlated with CONT_DOY and 2 other fieldsHigh correlation
STAT_CAUSE_CODE is highly correlated with OBJECTID and 8 other fieldsHigh correlation
CONT_DATE is highly correlated with OBJECTID and 5 other fieldsHigh correlation
CONT_DOY is highly correlated with DISCOVERY_DOY and 2 other fieldsHigh correlation
LATITUDE is highly correlated with OBJECTID and 6 other fieldsHigh correlation
LONGITUDE is highly correlated with OBJECTID and 9 other fieldsHigh correlation
OWNER_CODE is highly correlated with OBJECTID and 7 other fieldsHigh correlation
SOURCE_SYSTEM_TYPE is highly correlated with OBJECTID and 9 other fieldsHigh correlation
SOURCE_SYSTEM is highly correlated with OBJECTID and 13 other fieldsHigh correlation
NWCG_REPORTING_AGENCY is highly correlated with OBJECTID and 7 other fieldsHigh correlation
STAT_CAUSE_DESCR is highly correlated with SOURCE_SYSTEM_TYPE and 3 other fieldsHigh correlation
OWNER_DESCR is highly correlated with OBJECTID and 6 other fieldsHigh correlation
STATE is highly correlated with OBJECTID and 14 other fieldsHigh correlation
FIPS_CODE is highly correlated with STATEHigh correlation
LOCAL_FIRE_REPORT_ID has 486342 (77.6%) missing values Missing
LOCAL_INCIDENT_ID has 273458 (43.6%) missing values Missing
FIRE_CODE has 518312 (82.7%) missing values Missing
FIRE_NAME has 318923 (50.9%) missing values Missing
ICS_209_INCIDENT_NUMBER has 618192 (98.6%) missing values Missing
ICS_209_NAME has 618192 (98.6%) missing values Missing
MTBS_ID has 623132 (99.4%) missing values Missing
MTBS_FIRE_NAME has 623132 (99.4%) missing values Missing
COMPLEX_NAME has 625075 (99.7%) missing values Missing
DISCOVERY_TIME has 294076 (46.9%) missing values Missing
CONT_DATE has 297088 (47.4%) missing values Missing
CONT_DOY has 297088 (47.4%) missing values Missing
CONT_TIME has 323724 (51.6%) missing values Missing
COUNTY has 226115 (36.1%) missing values Missing
FIPS_CODE has 226115 (36.1%) missing values Missing
FIPS_NAME has 226115 (36.1%) missing values Missing
FIRE_SIZE is highly skewed (γ1 = 103.4342329) Skewed
FPA_ID is uniformly distributed Uniform
ICS_209_INCIDENT_NUMBER is uniformly distributed Uniform
ICS_209_NAME is uniformly distributed Uniform
MTBS_ID is uniformly distributed Uniform
OBJECTID has unique values Unique
FOD_ID has unique values Unique
FPA_ID has unique values Unique
Shape is an unsupported type, check if it needs cleaning or further analysis Unsupported

Reproduction

Analysis started2022-11-23 15:17:48.815072
Analysis finished2022-11-23 15:22:43.189677
Duration4 minutes and 54.37 seconds
Software versionpandas-profiling v3.4.0
Download configurationconfig.json

Variables

OBJECTID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct626822
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean940335.4853
Minimum1
Maximum1880465
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2022-11-23T17:22:43.236167image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile93434.15
Q1470600.5
median940745.5
Q31410315.75
95-th percentile1786115.9
Maximum1880465
Range1880464
Interquartile range (IQR)939715.25

Descriptive statistics

Standard deviation542780.9895
Coefficient of variation (CV)0.577220575
Kurtosis-1.198672587
Mean940335.4853
Median Absolute Deviation (MAD)469844
Skewness-0.001742082653
Sum5.894229696 × 1011
Variance2.946112026 × 1011
MonotonicityNot monotonic
2022-11-23T17:22:43.306848image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4634451
 
< 0.1%
3684081
 
< 0.1%
2940591
 
< 0.1%
1711621
 
< 0.1%
7473751
 
< 0.1%
4470601
 
< 0.1%
11569671
 
< 0.1%
8942651
 
< 0.1%
2350581
 
< 0.1%
6892591
 
< 0.1%
Other values (626812)626812
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
51
< 0.1%
91
< 0.1%
141
< 0.1%
161
< 0.1%
191
< 0.1%
211
< 0.1%
231
< 0.1%
241
< 0.1%
301
< 0.1%
ValueCountFrequency (%)
18804651
< 0.1%
18804631
< 0.1%
18804611
< 0.1%
18804601
< 0.1%
18804561
< 0.1%
18804531
< 0.1%
18804511
< 0.1%
18804501
< 0.1%
18804461
< 0.1%
18804451
< 0.1%

FOD_ID
Real number (ℝ≥0)

HIGH CORRELATION
UNIQUE

Distinct626822
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54770208.16
Minimum1
Maximum300348399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2022-11-23T17:22:43.381512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile94469.15
Q1505988.5
median1068273.5
Q319106352.75
95-th percentile300145252.9
Maximum300348399
Range300348398
Interquartile range (IQR)18600364.25

Descriptive statistics

Standard deviation101111818.9
Coefficient of variation (CV)1.846109816
Kurtosis0.5897490461
Mean54770208.16
Median Absolute Deviation (MAD)700380
Skewness1.512582709
Sum3.433117142 × 1013
Variance1.022359992 × 1016
MonotonicityNot monotonic
2022-11-23T17:22:43.448030image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4979591
 
< 0.1%
3799641
 
< 0.1%
2996131
 
< 0.1%
1728391
 
< 0.1%
8469311
 
< 0.1%
4812771
 
< 0.1%
14079361
 
< 0.1%
10200111
 
< 0.1%
2392211
 
< 0.1%
7668431
 
< 0.1%
Other values (626812)626812
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
51
< 0.1%
91
< 0.1%
141
< 0.1%
161
< 0.1%
191
< 0.1%
211
< 0.1%
231
< 0.1%
241
< 0.1%
301
< 0.1%
ValueCountFrequency (%)
3003483991
< 0.1%
3003483751
< 0.1%
3003483631
< 0.1%
3003483621
< 0.1%
3003483111
< 0.1%
3003482931
< 0.1%
3003482901
< 0.1%
3003482891
< 0.1%
3003482591
< 0.1%
3003482581
< 0.1%

FPA_ID
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct626822
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
SFO-ID0043_3101998026
 
1
FWS-2004SCCRRA3YK
 
1
W-347649
 
1
FS-401647
 
1
SC_26795
 
1
Other values (626817)
626817 

Length

Max length49
Median length37
Mean length16.53867127
Min length3

Characters and Unicode

Total characters10366803
Distinct characters71
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique626822 ?
Unique (%)100.0%

Sample

1st rowSFO-ID0043_3101998026
2nd rowW-626311
3rd rowSFO-NE-2012-16029
4th rowSWRA_VA_11300
5th rowSWRA_LA_24392

Common Values

ValueCountFrequency (%)
SFO-ID0043_31019980261
 
< 0.1%
FWS-2004SCCRRA3YK1
 
< 0.1%
W-3476491
 
< 0.1%
FS-4016471
 
< 0.1%
SC_267951
 
< 0.1%
SFO-GA0261-36-315-0007-081
 
< 0.1%
ALS-SEL-20040316-0051
 
< 0.1%
SWRA_LA_222501
 
< 0.1%
W-198091
 
< 0.1%
ODF-626221
 
< 0.1%
Other values (626812)626812
> 99.9%

Length

2022-11-23T17:22:43.540034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sfo-2013ladaf332
 
0.1%
sfo-2010-vavas317
 
0.1%
sfo-2014vavas274
 
< 0.1%
sfo-2014ladaf267
 
< 0.1%
2011vavas255
 
< 0.1%
sfo-2013vavas192
 
< 0.1%
sfo-2015vavas187
 
< 0.1%
2011gagas-fy2011-jeff29
 
< 0.1%
sfo-ga-fy2001-bryan27
 
< 0.1%
sfo-ga-fy2002-bryan25
 
< 0.1%
Other values (626664)627210
99.7%

Most occurring characters

ValueCountFrequency (%)
01256591
 
12.1%
-972480
 
9.4%
1820595
 
7.9%
2794546
 
7.7%
S571498
 
5.5%
F525413
 
5.1%
3483159
 
4.7%
5464743
 
4.5%
4462004
 
4.5%
9405367
 
3.9%
Other values (61)3610407
34.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number5671346
54.7%
Uppercase Letter3060963
29.5%
Dash Punctuation972480
 
9.4%
Connector Punctuation326406
 
3.1%
Space Separator191961
 
1.9%
Lowercase Letter103833
 
1.0%
Other Punctuation36521
 
0.4%
Open Punctuation1645
 
< 0.1%
Close Punctuation1645
 
< 0.1%
Math Symbol2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S571498
18.7%
F525413
17.2%
O273983
9.0%
A240471
7.9%
T182405
 
6.0%
C168237
 
5.5%
W162485
 
5.3%
N150149
 
4.9%
D98032
 
3.2%
R89426
 
2.9%
Other values (16)598864
19.6%
Lowercase Letter
ValueCountFrequency (%)
e11762
11.3%
n10168
9.8%
o9900
 
9.5%
a9758
 
9.4%
r9156
 
8.8%
l7825
 
7.5%
i6147
 
5.9%
t5750
 
5.5%
s4783
 
4.6%
h3631
 
3.5%
Other values (14)24953
24.0%
Decimal Number
ValueCountFrequency (%)
01256591
22.2%
1820595
14.5%
2794546
14.0%
3483159
 
8.5%
5464743
 
8.2%
4462004
 
8.1%
9405367
 
7.1%
6379441
 
6.7%
7309429
 
5.5%
8295471
 
5.2%
Other Punctuation
ValueCountFrequency (%)
/35835
98.1%
.354
 
1.0%
,332
 
0.9%
Math Symbol
ValueCountFrequency (%)
=1
50.0%
+1
50.0%
Dash Punctuation
ValueCountFrequency (%)
-972480
100.0%
Connector Punctuation
ValueCountFrequency (%)
_326406
100.0%
Space Separator
ValueCountFrequency (%)
191961
100.0%
Open Punctuation
ValueCountFrequency (%)
(1645
100.0%
Close Punctuation
ValueCountFrequency (%)
)1645
100.0%
Modifier Symbol
ValueCountFrequency (%)
`1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common7202007
69.5%
Latin3164796
30.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
S571498
18.1%
F525413
16.6%
O273983
 
8.7%
A240471
 
7.6%
T182405
 
5.8%
C168237
 
5.3%
W162485
 
5.1%
N150149
 
4.7%
D98032
 
3.1%
R89426
 
2.8%
Other values (40)702697
22.2%
Common
ValueCountFrequency (%)
01256591
17.4%
-972480
13.5%
1820595
11.4%
2794546
11.0%
3483159
 
6.7%
5464743
 
6.5%
4462004
 
6.4%
9405367
 
5.6%
6379441
 
5.3%
_326406
 
4.5%
Other values (11)836675
11.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII10366803
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01256591
 
12.1%
-972480
 
9.4%
1820595
 
7.9%
2794546
 
7.7%
S571498
 
5.5%
F525413
 
5.1%
3483159
 
4.7%
5464743
 
4.5%
4462004
 
4.5%
9405367
 
3.9%
Other values (61)3610407
34.8%

SOURCE_SYSTEM_TYPE
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
NONFED
453909 
FED
160422 
INTERAGCY
 
12491

Length

Max length9
Median length6
Mean length5.291995176
Min length3

Characters and Unicode

Total characters3317139
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNONFED
2nd rowFED
3rd rowNONFED
4th rowNONFED
5th rowNONFED

Common Values

ValueCountFrequency (%)
NONFED453909
72.4%
FED160422
 
25.6%
INTERAGCY12491
 
2.0%

Length

2022-11-23T17:22:43.601799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-23T17:22:43.656094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
nonfed453909
72.4%
fed160422
 
25.6%
interagcy12491
 
2.0%

Most occurring characters

ValueCountFrequency (%)
N920309
27.7%
E626822
18.9%
F614331
18.5%
D614331
18.5%
O453909
13.7%
I12491
 
0.4%
T12491
 
0.4%
R12491
 
0.4%
A12491
 
0.4%
G12491
 
0.4%
Other values (2)24982
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3317139
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N920309
27.7%
E626822
18.9%
F614331
18.5%
D614331
18.5%
O453909
13.7%
I12491
 
0.4%
T12491
 
0.4%
R12491
 
0.4%
A12491
 
0.4%
G12491
 
0.4%
Other values (2)24982
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin3317139
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N920309
27.7%
E626822
18.9%
F614331
18.5%
D614331
18.5%
O453909
13.7%
I12491
 
0.4%
T12491
 
0.4%
R12491
 
0.4%
A12491
 
0.4%
G12491
 
0.4%
Other values (2)24982
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII3317139
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N920309
27.7%
E626822
18.9%
F614331
18.5%
D614331
18.5%
O453909
13.7%
I12491
 
0.4%
T12491
 
0.4%
R12491
 
0.4%
A12491
 
0.4%
G12491
 
0.4%
Other values (2)24982
 
0.8%

SOURCE_SYSTEM
Categorical

HIGH CORRELATION

Distinct38
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
ST-NASF
236695 
DOI-WFMI
80153 
FS-FIRESTAT
73777 
ST-CACDF
29220 
ST-NCNCS
 
21958
Other values (33)
185019 

Length

Max length11
Median length9
Mean length7.988255039
Min length7

Characters and Unicode

Total characters5007214
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowST-NASF
2nd rowDOI-WFMI
3rd rowST-NASF
4th rowST-VAVAS
5th rowST-LALAS

Common Values

ValueCountFrequency (%)
ST-NASF236695
37.8%
DOI-WFMI80153
 
12.8%
FS-FIRESTAT73777
 
11.8%
ST-CACDF29220
 
4.7%
ST-NCNCS21958
 
3.5%
ST-GAGAS21708
 
3.5%
ST-MSMSS20041
 
3.2%
ST-TXTXS19373
 
3.1%
ST-ALALS18486
 
2.9%
ST-SCSCS16326
 
2.6%
Other values (28)89085
 
14.2%

Length

2022-11-23T17:22:43.705036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st-nasf236695
37.8%
doi-wfmi80153
 
12.8%
fs-firestat73777
 
11.8%
st-cacdf29220
 
4.7%
st-ncncs21958
 
3.5%
st-gagas21708
 
3.5%
st-msmss20041
 
3.2%
st-txtxs19373
 
3.1%
st-alals18486
 
2.9%
st-scscs16326
 
2.6%
Other values (28)89085
 
14.2%

Most occurring characters

ValueCountFrequency (%)
S1113641
22.2%
T663286
13.2%
-626822
12.5%
F546087
10.9%
A470253
9.4%
N294353
 
5.9%
I283827
 
5.7%
M147395
 
2.9%
C142071
 
2.8%
O114199
 
2.3%
Other values (17)605280
12.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4378367
87.4%
Dash Punctuation626822
 
12.5%
Decimal Number2025
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S1113641
25.4%
T663286
15.1%
F546087
12.5%
A470253
10.7%
N294353
 
6.7%
I283827
 
6.5%
M147395
 
3.4%
C142071
 
3.2%
O114199
 
2.6%
D111583
 
2.5%
Other values (13)491672
11.2%
Decimal Number
ValueCountFrequency (%)
2675
33.3%
0675
33.3%
9675
33.3%
Dash Punctuation
ValueCountFrequency (%)
-626822
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin4378367
87.4%
Common628847
 
12.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
S1113641
25.4%
T663286
15.1%
F546087
12.5%
A470253
10.7%
N294353
 
6.7%
I283827
 
6.5%
M147395
 
3.4%
C142071
 
3.2%
O114199
 
2.6%
D111583
 
2.5%
Other values (13)491672
11.2%
Common
ValueCountFrequency (%)
-626822
99.7%
2675
 
0.1%
0675
 
0.1%
9675
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5007214
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S1113641
22.2%
T663286
13.2%
-626822
12.5%
F546087
10.9%
A470253
9.4%
N294353
 
5.9%
I283827
 
5.7%
M147395
 
2.9%
C142071
 
2.8%
O114199
 
2.3%
Other values (17)605280
12.1%

NWCG_REPORTING_AGENCY
Categorical

HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
ST/C&L
458921 
FS
73824 
BIA
 
39753
BLM
 
32350
IA
 
7344
Other values (6)
 
14630

Length

Max length6
Median length6
Mean length5.070764906
Min length2

Characters and Unicode

Total characters3178467
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowST/C&L
2nd rowBIA
3rd rowST/C&L
4th rowST/C&L
5th rowST/C&L

Common Values

ValueCountFrequency (%)
ST/C&L458921
73.2%
FS73824
 
11.8%
BIA39753
 
6.3%
BLM32350
 
5.2%
IA7344
 
1.2%
NPS6899
 
1.1%
FWS6495
 
1.0%
TRIBE1203
 
0.2%
DOD25
 
< 0.1%
BOR7
 
< 0.1%

Length

2022-11-23T17:22:43.763468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
st/c&l458921
73.2%
fs73824
 
11.8%
bia39753
 
6.3%
blm32350
 
5.2%
ia7344
 
1.2%
nps6899
 
1.1%
fws6495
 
1.0%
tribe1203
 
0.2%
dod25
 
< 0.1%
bor7
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
S546139
17.2%
L491271
15.5%
T460124
14.5%
/458921
14.4%
C458921
14.4%
&458921
14.4%
F80319
 
2.5%
B73313
 
2.3%
I48300
 
1.5%
A47097
 
1.5%
Other values (8)55141
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2260625
71.1%
Other Punctuation917842
28.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S546139
24.2%
L491271
21.7%
T460124
20.4%
C458921
20.3%
F80319
 
3.6%
B73313
 
3.2%
I48300
 
2.1%
A47097
 
2.1%
M32350
 
1.4%
N6899
 
0.3%
Other values (6)15892
 
0.7%
Other Punctuation
ValueCountFrequency (%)
/458921
50.0%
&458921
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2260625
71.1%
Common917842
28.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
S546139
24.2%
L491271
21.7%
T460124
20.4%
C458921
20.3%
F80319
 
3.6%
B73313
 
3.2%
I48300
 
2.1%
A47097
 
2.1%
M32350
 
1.4%
N6899
 
0.3%
Other values (6)15892
 
0.7%
Common
ValueCountFrequency (%)
/458921
50.0%
&458921
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3178467
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S546139
17.2%
L491271
15.5%
T460124
14.5%
/458921
14.4%
C458921
14.4%
&458921
14.4%
F80319
 
2.5%
B73313
 
2.3%
I48300
 
1.5%
A47097
 
1.5%
Other values (8)55141
 
1.7%

NWCG_REPORTING_UNIT_ID
Categorical

HIGH CARDINALITY

Distinct1405
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
USGAGAS
55700 
USTXTXS
 
37055
USNCNCS
 
35857
USFLFLS
 
27717
USSCSCS
 
26131
Other values (1400)
444362 

Length

Max length9
Median length7
Mean length7.030603584
Min length7

Characters and Unicode

Total characters4406937
Distinct characters36
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique220 ?
Unique (%)< 0.1%

Sample

1st rowUSIDIDS
2nd rowUSMTFBA
3rd rowUSNENES
4th rowUSVAVAS
5th rowUSLALAS

Common Values

ValueCountFrequency (%)
USGAGAS55700
 
8.9%
USTXTXS37055
 
5.9%
USNCNCS35857
 
5.7%
USFLFLS27717
 
4.4%
USSCSCS26131
 
4.2%
USNYNYX25062
 
4.0%
USMSMSS24854
 
4.0%
USALALS21733
 
3.5%
USOKOKS9878
 
1.6%
USMNMNS9843
 
1.6%
Other values (1395)352992
56.3%

Length

2022-11-23T17:22:43.820322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usgagas55700
 
8.9%
ustxtxs37055
 
5.9%
usncncs35857
 
5.7%
usflfls27717
 
4.4%
usscscs26131
 
4.2%
usnynyx25062
 
4.0%
usmsmss24854
 
4.0%
usalals21733
 
3.5%
usokoks9878
 
1.6%
usmnmns9843
 
1.6%
Other values (1395)352992
56.3%

Most occurring characters

ValueCountFrequency (%)
S1170326
26.6%
U686806
15.6%
A399140
 
9.1%
N269862
 
6.1%
C250512
 
5.7%
T169007
 
3.8%
M168735
 
3.8%
F155813
 
3.5%
L144702
 
3.3%
G121319
 
2.8%
Other values (26)870715
19.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4388845
99.6%
Decimal Number18092
 
0.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S1170326
26.7%
U686806
15.6%
A399140
 
9.1%
N269862
 
6.1%
C250512
 
5.7%
T169007
 
3.9%
M168735
 
3.8%
F155813
 
3.6%
L144702
 
3.3%
G121319
 
2.8%
Other values (16)852623
19.4%
Decimal Number
ValueCountFrequency (%)
13680
20.3%
73556
19.7%
52679
14.8%
22336
12.9%
92313
12.8%
81233
 
6.8%
31096
 
6.1%
0753
 
4.2%
4420
 
2.3%
626
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin4388845
99.6%
Common18092
 
0.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
S1170326
26.7%
U686806
15.6%
A399140
 
9.1%
N269862
 
6.1%
C250512
 
5.7%
T169007
 
3.9%
M168735
 
3.8%
F155813
 
3.6%
L144702
 
3.3%
G121319
 
2.8%
Other values (16)852623
19.4%
Common
ValueCountFrequency (%)
13680
20.3%
73556
19.7%
52679
14.8%
22336
12.9%
92313
12.8%
81233
 
6.8%
31096
 
6.1%
0753
 
4.2%
4420
 
2.3%
626
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4406937
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S1170326
26.6%
U686806
15.6%
A399140
 
9.1%
N269862
 
6.1%
C250512
 
5.7%
T169007
 
3.8%
M168735
 
3.8%
F155813
 
3.5%
L144702
 
3.3%
G121319
 
2.8%
Other values (26)870715
19.8%

NWCG_REPORTING_UNIT_NAME
Categorical

HIGH CARDINALITY

Distinct1401
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
Georgia Forestry Commission
55700 
Texas A & M Forest Service
 
37055
North Carolina Forest Service
 
35857
Florida Forest Service
 
27717
South Carolina Forestry Commission
 
26131
Other values (1396)
444362 

Length

Max length79
Median length62
Mean length27.3971957
Min length5

Characters and Unicode

Total characters17173165
Distinct characters65
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique219 ?
Unique (%)< 0.1%

Sample

1st rowIdaho Department of Lands
2nd rowFort Belknap Agency
3rd rowNebraska Department of Forestry
4th rowVirginia Department of Forestry
5th rowLouisiana Office of Forestry

Common Values

ValueCountFrequency (%)
Georgia Forestry Commission55700
 
8.9%
Texas A & M Forest Service37055
 
5.9%
North Carolina Forest Service35857
 
5.7%
Florida Forest Service27717
 
4.4%
South Carolina Forestry Commission26131
 
4.2%
Fire Department of New York25062
 
4.0%
Mississippi Forestry Commission24854
 
4.0%
Alabama Forestry Commission21733
 
3.5%
Oklahoma Division of Forestry9878
 
1.6%
Minnesota Department of Natural Resources9843
 
1.6%
Other values (1391)352992
56.3%

Length

2022-11-23T17:22:43.897340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
forestry218290
 
9.3%
forest195577
 
8.3%
commission137699
 
5.8%
of124657
 
5.3%
service122624
 
5.2%
national85508
 
3.6%
department68566
 
2.9%
carolina62808
 
2.7%
57673
 
2.4%
georgia55700
 
2.4%
Other values (1400)1229600
52.1%

Most occurring characters

ValueCountFrequency (%)
1733393
 
10.1%
e1582121
 
9.2%
o1510202
 
8.8%
r1421522
 
8.3%
i1403158
 
8.2%
s1235206
 
7.2%
t1114039
 
6.5%
a1107697
 
6.5%
n862110
 
5.0%
F523997
 
3.1%
Other values (55)4679720
27.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13125614
76.4%
Uppercase Letter2212146
 
12.9%
Space Separator1733393
 
10.1%
Dash Punctuation55644
 
0.3%
Other Punctuation46309
 
0.3%
Open Punctuation26
 
< 0.1%
Close Punctuation26
 
< 0.1%
Decimal Number7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1582121
12.1%
o1510202
11.5%
r1421522
10.8%
i1403158
10.7%
s1235206
9.4%
t1114039
8.5%
a1107697
8.4%
n862110
6.6%
m409501
 
3.1%
l391303
 
3.0%
Other values (16)2088755
15.9%
Uppercase Letter
ValueCountFrequency (%)
F523997
23.7%
C259122
11.7%
S225020
10.2%
N207863
 
9.4%
D156934
 
7.1%
A134677
 
6.1%
M125335
 
5.7%
T86619
 
3.9%
G66478
 
3.0%
R58016
 
2.6%
Other values (15)368085
16.6%
Other Punctuation
ValueCountFrequency (%)
&39250
84.8%
/3829
 
8.3%
.1647
 
3.6%
'1576
 
3.4%
"4
 
< 0.1%
#3
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
13
42.9%
42
28.6%
01
 
14.3%
51
 
14.3%
Space Separator
ValueCountFrequency (%)
1733393
100.0%
Dash Punctuation
ValueCountFrequency (%)
-55644
100.0%
Open Punctuation
ValueCountFrequency (%)
(26
100.0%
Close Punctuation
ValueCountFrequency (%)
)26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin15337760
89.3%
Common1835405
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1582121
 
10.3%
o1510202
 
9.8%
r1421522
 
9.3%
i1403158
 
9.1%
s1235206
 
8.1%
t1114039
 
7.3%
a1107697
 
7.2%
n862110
 
5.6%
F523997
 
3.4%
m409501
 
2.7%
Other values (41)4168207
27.2%
Common
ValueCountFrequency (%)
1733393
94.4%
-55644
 
3.0%
&39250
 
2.1%
/3829
 
0.2%
.1647
 
0.1%
'1576
 
0.1%
(26
 
< 0.1%
)26
 
< 0.1%
"4
 
< 0.1%
#3
 
< 0.1%
Other values (4)7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII17173165
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1733393
 
10.1%
e1582121
 
9.2%
o1510202
 
8.8%
r1421522
 
8.3%
i1403158
 
8.2%
s1235206
 
7.2%
t1114039
 
6.5%
a1107697
 
6.5%
n862110
 
5.0%
F523997
 
3.1%
Other values (55)4679720
27.3%

SOURCE_REPORTING_UNIT
Categorical

HIGH CARDINALITY

Distinct4262
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
GAGAS
 
32623
SCSCS
 
17158
TXTXS
 
13430
FLFLS
 
12753
NCNCS
 
12475
Other values (4257)
538383 

Length

Max length21
Median length5
Mean length5.571848148
Min length2

Characters and Unicode

Total characters3492557
Distinct characters62
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique692 ?
Unique (%)0.1%

Sample

1st rowID310
2nd rowMTFBA
3rd rowNENFS
4th rowVAVAS3
5th rowLALAS1

Common Values

ValueCountFrequency (%)
GAGAS32623
 
5.2%
SCSCS17158
 
2.7%
TXTXS13430
 
2.1%
FLFLS12753
 
2.0%
NCNCS12475
 
2.0%
TXVFD12001
 
1.9%
MSMSS10535
 
1.7%
MNMNS8029
 
1.3%
PRIITF7327
 
1.2%
WVDOF5694
 
0.9%
Other values (4252)494797
78.9%

Length

2022-11-23T17:22:43.970709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gagas32623
 
4.9%
ga17178
 
2.6%
scscs17158
 
2.6%
txtxs13430
 
2.0%
flfls12753
 
1.9%
ncncs12475
 
1.9%
txvfd12001
 
1.8%
msmss10535
 
1.6%
ms9524
 
1.4%
mnmns8029
 
1.2%
Other values (4268)521304
78.2%

Most occurring characters

ValueCountFrequency (%)
S410939
 
11.8%
A341002
 
9.8%
C232661
 
6.7%
N211638
 
6.1%
0158976
 
4.6%
L134456
 
3.8%
F131312
 
3.8%
M130553
 
3.7%
T127212
 
3.6%
D116731
 
3.3%
Other values (52)1497077
42.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2615057
74.9%
Decimal Number576007
 
16.5%
Lowercase Letter252390
 
7.2%
Space Separator41171
 
1.2%
Dash Punctuation7226
 
0.2%
Connector Punctuation702
 
< 0.1%
Other Punctuation4
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S410939
15.7%
A341002
13.0%
C232661
 
8.9%
N211638
 
8.1%
L134456
 
5.1%
F131312
 
5.0%
M130553
 
5.0%
T127212
 
4.9%
D116731
 
4.5%
G104103
 
4.0%
Other values (16)674450
25.8%
Lowercase Letter
ValueCountFrequency (%)
t29029
11.5%
e28842
11.4%
a28323
11.2%
s21986
8.7%
o21260
8.4%
l17610
 
7.0%
i16213
 
6.4%
r14447
 
5.7%
h12850
 
5.1%
u10805
 
4.3%
Other values (12)51025
20.2%
Decimal Number
ValueCountFrequency (%)
0158976
27.6%
1104114
18.1%
265864
11.4%
354303
 
9.4%
445271
 
7.9%
543940
 
7.6%
635884
 
6.2%
826549
 
4.6%
721507
 
3.7%
919599
 
3.4%
Space Separator
ValueCountFrequency (%)
41171
100.0%
Dash Punctuation
ValueCountFrequency (%)
-7226
100.0%
Connector Punctuation
ValueCountFrequency (%)
_702
100.0%
Other Punctuation
ValueCountFrequency (%)
/4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2867447
82.1%
Common625110
 
17.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
S410939
14.3%
A341002
 
11.9%
C232661
 
8.1%
N211638
 
7.4%
L134456
 
4.7%
F131312
 
4.6%
M130553
 
4.6%
T127212
 
4.4%
D116731
 
4.1%
G104103
 
3.6%
Other values (38)926840
32.3%
Common
ValueCountFrequency (%)
0158976
25.4%
1104114
16.7%
265864
10.5%
354303
 
8.7%
445271
 
7.2%
543940
 
7.0%
41171
 
6.6%
635884
 
5.7%
826549
 
4.2%
721507
 
3.4%
Other values (4)27531
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3492557
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S410939
 
11.8%
A341002
 
9.8%
C232661
 
6.7%
N211638
 
6.1%
0158976
 
4.6%
L134456
 
3.8%
F131312
 
3.8%
M130553
 
3.7%
T127212
 
3.6%
D116731
 
3.3%
Other values (52)1497077
42.9%

SOURCE_REPORTING_UNIT_NAME
Categorical

HIGH CARDINALITY

Distinct3737
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
Georgia Forestry Commission
 
32623
Fire Department of New York
 
25062
South Carolina Forestry Commission
 
17158
Mississippi Forestry Commission
 
15330
Texas Forest Service
 
14203
Other values (3732)
522446 

Length

Max length72
Median length55
Mean length26.00516415
Min length5

Characters and Unicode

Total characters16300609
Distinct characters75
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique640 ?
Unique (%)0.1%

Sample

1st rowIDS District 310
2nd rowFort Belknap Agency
3rd rowNebraska Forest Service
4th rowFarmville District
5th rowLAS District 1

Common Values

ValueCountFrequency (%)
Georgia Forestry Commission32623
 
5.2%
Fire Department of New York25062
 
4.0%
South Carolina Forestry Commission17158
 
2.7%
Mississippi Forestry Commission15330
 
2.4%
Texas Forest Service14203
 
2.3%
North Carolina Division of Forest Resources13239
 
2.1%
Florida Forest Service12753
 
2.0%
Minnesota Department of Natural Resources9843
 
1.6%
International Institute of Tropical Forestry7327
 
1.2%
Alabama Forestry Commission7109
 
1.1%
Other values (3727)472175
75.3%

Length

2022-11-23T17:22:44.043447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
forestry137764
 
5.9%
forest137398
 
5.9%
district110285
 
4.7%
of109465
 
4.7%
national83755
 
3.6%
commission75758
 
3.2%
department73061
 
3.1%
unit62852
 
2.7%
fire58206
 
2.5%
service53154
 
2.3%
Other values (2932)1439101
61.5%

Most occurring characters

ValueCountFrequency (%)
1714192
 
10.5%
e1446439
 
8.9%
i1337163
 
8.2%
o1212411
 
7.4%
r1195649
 
7.3%
t1194140
 
7.3%
s1009199
 
6.2%
a962480
 
5.9%
n838392
 
5.1%
F441314
 
2.7%
Other values (65)4949230
30.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter11890763
72.9%
Uppercase Letter2477060
 
15.2%
Space Separator1714192
 
10.5%
Decimal Number106476
 
0.7%
Dash Punctuation73475
 
0.5%
Other Punctuation29048
 
0.2%
Open Punctuation4786
 
< 0.1%
Close Punctuation4786
 
< 0.1%
Modifier Symbol23
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e1446439
12.2%
i1337163
11.2%
o1212411
10.2%
r1195649
10.1%
t1194140
10.0%
s1009199
8.5%
a962480
8.1%
n838392
7.1%
l395201
 
3.3%
c394183
 
3.3%
Other values (17)1905506
16.0%
Uppercase Letter
ValueCountFrequency (%)
F441314
17.8%
S289000
11.7%
D285357
11.5%
C258802
10.4%
N211460
8.5%
A145141
 
5.9%
R108645
 
4.4%
M95379
 
3.9%
U71894
 
2.9%
G71508
 
2.9%
Other values (16)498560
20.1%
Decimal Number
ValueCountFrequency (%)
227259
25.6%
121891
20.6%
319546
18.4%
49008
 
8.5%
66768
 
6.4%
56134
 
5.8%
04837
 
4.5%
84557
 
4.3%
93291
 
3.1%
73185
 
3.0%
Other Punctuation
ValueCountFrequency (%)
,19581
67.4%
.5951
 
20.5%
&2060
 
7.1%
/580
 
2.0%
'554
 
1.9%
#318
 
1.1%
"4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1714192
100.0%
Dash Punctuation
ValueCountFrequency (%)
-73475
100.0%
Open Punctuation
ValueCountFrequency (%)
(4786
100.0%
Close Punctuation
ValueCountFrequency (%)
)4786
100.0%
Modifier Symbol
ValueCountFrequency (%)
`23
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin14367823
88.1%
Common1932786
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e1446439
 
10.1%
i1337163
 
9.3%
o1212411
 
8.4%
r1195649
 
8.3%
t1194140
 
8.3%
s1009199
 
7.0%
a962480
 
6.7%
n838392
 
5.8%
F441314
 
3.1%
l395201
 
2.8%
Other values (43)4335435
30.2%
Common
ValueCountFrequency (%)
1714192
88.7%
-73475
 
3.8%
227259
 
1.4%
121891
 
1.1%
,19581
 
1.0%
319546
 
1.0%
49008
 
0.5%
66768
 
0.4%
56134
 
0.3%
.5951
 
0.3%
Other values (12)28981
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII16300597
> 99.9%
None12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1714192
 
10.5%
e1446439
 
8.9%
i1337163
 
8.2%
o1212411
 
7.4%
r1195649
 
7.3%
t1194140
 
7.3%
s1009199
 
6.2%
a962480
 
5.9%
n838392
 
5.1%
F441314
 
2.7%
Other values (64)4949218
30.4%
None
ValueCountFrequency (%)
ñ12
100.0%

LOCAL_FIRE_REPORT_ID
Categorical

HIGH CARDINALITY
MISSING

Distinct6189
Distinct (%)4.4%
Missing486342
Missing (%)77.6%
Memory size4.8 MiB
001
 
2786
002
 
1661
2
 
1231
1
 
1176
5
 
1165
Other values (6184)
132461 

Length

Max length6
Median length5
Mean length2.499345103
Min length1

Characters and Unicode

Total characters351108
Distinct characters30
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4207 ?
Unique (%)3.0%

Sample

1st row42
2nd row25
3rd row146
4th row10
5th row36

Common Values

ValueCountFrequency (%)
0012786
 
0.4%
0021661
 
0.3%
21231
 
0.2%
11176
 
0.2%
51165
 
0.2%
81132
 
0.2%
31119
 
0.2%
61115
 
0.2%
0031103
 
0.2%
41095
 
0.2%
Other values (6179)126897
 
20.2%
(Missing)486342
77.6%

Length

2022-11-23T17:22:44.109751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0012786
 
2.0%
0021661
 
1.2%
21231
 
0.9%
11176
 
0.8%
51165
 
0.8%
81132
 
0.8%
31119
 
0.8%
61115
 
0.8%
0031103
 
0.8%
41095
 
0.8%
Other values (6178)126897
90.3%

Most occurring characters

ValueCountFrequency (%)
163641
18.1%
052900
15.1%
242308
12.0%
334777
9.9%
431763
9.0%
528846
8.2%
626189
7.5%
724306
 
6.9%
823050
 
6.6%
922784
 
6.5%
Other values (20)544
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number350564
99.8%
Uppercase Letter543
 
0.2%
Dash Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B79
14.5%
C68
12.5%
A66
12.2%
D38
 
7.0%
F30
 
5.5%
P29
 
5.3%
J28
 
5.2%
T27
 
5.0%
E27
 
5.0%
H25
 
4.6%
Other values (9)126
23.2%
Decimal Number
ValueCountFrequency (%)
163641
18.2%
052900
15.1%
242308
12.1%
334777
9.9%
431763
9.1%
528846
8.2%
626189
7.5%
724306
 
6.9%
823050
 
6.6%
922784
 
6.5%
Dash Punctuation
ValueCountFrequency (%)
-1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common350565
99.8%
Latin543
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
B79
14.5%
C68
12.5%
A66
12.2%
D38
 
7.0%
F30
 
5.5%
P29
 
5.3%
J28
 
5.2%
T27
 
5.0%
E27
 
5.0%
H25
 
4.6%
Other values (9)126
23.2%
Common
ValueCountFrequency (%)
163641
18.2%
052900
15.1%
242308
12.1%
334777
9.9%
431763
9.1%
528846
8.2%
626189
7.5%
724306
 
6.9%
823050
 
6.6%
922784
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII351108
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
163641
18.1%
052900
15.1%
242308
12.0%
334777
9.9%
431763
9.0%
528846
8.2%
626189
7.5%
724306
 
6.9%
823050
 
6.6%
922784
 
6.5%
Other values (20)544
 
0.2%

LOCAL_INCIDENT_ID
Categorical

HIGH CARDINALITY
MISSING

Distinct213528
Distinct (%)60.4%
Missing273458
Missing (%)43.6%
Memory size4.8 MiB
001
 
1328
1
 
1033
2
 
963
3
 
854
4
 
826
Other values (213523)
348360 

Length

Max length28
Median length25
Mean length8.150793516
Min length1

Characters and Unicode

Total characters2880197
Distinct characters71
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique190380 ?
Unique (%)53.9%

Sample

1st row16029
2nd row9801
3rd rowLA1-12
4th row20320089011
5th row01-007

Common Values

ValueCountFrequency (%)
0011328
 
0.2%
11033
 
0.2%
2963
 
0.2%
3854
 
0.1%
4826
 
0.1%
002817
 
0.1%
5760
 
0.1%
6752
 
0.1%
10750
 
0.1%
12685
 
0.1%
Other values (213518)344596
55.0%
(Missing)273458
43.6%

Length

2022-11-23T17:22:44.175558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0011330
 
0.4%
11037
 
0.3%
2967
 
0.3%
3860
 
0.2%
4837
 
0.2%
002817
 
0.2%
5766
 
0.2%
10763
 
0.2%
6758
 
0.2%
12697
 
0.2%
Other values (208447)345674
97.5%

Most occurring characters

ValueCountFrequency (%)
0586537
20.4%
1308208
10.7%
2270177
9.4%
189641
 
6.6%
-184403
 
6.4%
3169192
 
5.9%
4158433
 
5.5%
5149529
 
5.2%
9132854
 
4.6%
6121004
 
4.2%
Other values (61)610219
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2131661
74.0%
Uppercase Letter259015
 
9.0%
Space Separator189641
 
6.6%
Dash Punctuation184403
 
6.4%
Lowercase Letter93564
 
3.2%
Other Punctuation18746
 
0.7%
Open Punctuation1413
 
< 0.1%
Close Punctuation1413
 
< 0.1%
Connector Punctuation339
 
< 0.1%
Math Symbol2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Y42584
16.4%
N35628
13.8%
F26754
10.3%
A19339
 
7.5%
S17913
 
6.9%
C15619
 
6.0%
M11401
 
4.4%
T11383
 
4.4%
L9762
 
3.8%
U9686
 
3.7%
Other values (16)58946
22.8%
Lowercase Letter
ValueCountFrequency (%)
e10585
11.3%
n9168
9.8%
o8920
 
9.5%
a8830
 
9.4%
r8236
 
8.8%
l6967
 
7.4%
i5525
 
5.9%
t5241
 
5.6%
s4307
 
4.6%
h3307
 
3.5%
Other values (14)22478
24.0%
Decimal Number
ValueCountFrequency (%)
0586537
27.5%
1308208
14.5%
2270177
12.7%
3169192
 
7.9%
4158433
 
7.4%
5149529
 
7.0%
9132854
 
6.2%
6121004
 
5.7%
7118392
 
5.6%
8117335
 
5.5%
Other Punctuation
ValueCountFrequency (%)
.13869
74.0%
/4509
 
24.1%
,332
 
1.8%
#28
 
0.1%
?8
 
< 0.1%
Space Separator
ValueCountFrequency (%)
189641
100.0%
Dash Punctuation
ValueCountFrequency (%)
-184403
100.0%
Open Punctuation
ValueCountFrequency (%)
(1413
100.0%
Close Punctuation
ValueCountFrequency (%)
)1413
100.0%
Connector Punctuation
ValueCountFrequency (%)
_339
100.0%
Math Symbol
ValueCountFrequency (%)
=2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2527618
87.8%
Latin352579
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
Y42584
 
12.1%
N35628
 
10.1%
F26754
 
7.6%
A19339
 
5.5%
S17913
 
5.1%
C15619
 
4.4%
M11401
 
3.2%
T11383
 
3.2%
e10585
 
3.0%
L9762
 
2.8%
Other values (40)151611
43.0%
Common
ValueCountFrequency (%)
0586537
23.2%
1308208
12.2%
2270177
10.7%
189641
 
7.5%
-184403
 
7.3%
3169192
 
6.7%
4158433
 
6.3%
5149529
 
5.9%
9132854
 
5.3%
6121004
 
4.8%
Other values (11)257640
10.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII2880197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0586537
20.4%
1308208
10.7%
2270177
9.4%
189641
 
6.6%
-184403
 
6.4%
3169192
 
5.9%
4158433
 
5.5%
5149529
 
5.2%
9132854
 
4.6%
6121004
 
4.2%
Other values (61)610219
21.2%

FIRE_CODE
Categorical

HIGH CARDINALITY
MISSING

Distinct65982
Distinct (%)60.8%
Missing518312
Missing (%)82.7%
Memory size4.8 MiB
D44Z
 
3161
5555
 
1770
D5GJ
 
1159
0001
 
1135
0000
 
629
Other values (65977)
100656 

Length

Max length6
Median length4
Mean length3.997834301
Min length0

Characters and Unicode

Total characters433805
Distinct characters47
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59165 ?
Unique (%)54.5%

Sample

1st rowGB2S
2nd rowB2JN
3rd rowBEX6
4th rowD44Z
5th rowG197

Common Values

ValueCountFrequency (%)
D44Z3161
 
0.5%
55551770
 
0.3%
D5GJ1159
 
0.2%
00011135
 
0.2%
0000629
 
0.1%
2300621
 
0.1%
4700350
 
0.1%
EKV3340
 
0.1%
EKT5315
 
0.1%
0100314
 
0.1%
Other values (65972)98716
 
15.7%
(Missing)518312
82.7%

Length

2022-11-23T17:22:44.240648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
d44z3161
 
2.9%
55551770
 
1.6%
d5gj1159
 
1.1%
00011135
 
1.0%
0000629
 
0.6%
2300621
 
0.6%
4700350
 
0.3%
ekv3340
 
0.3%
ekt5315
 
0.3%
0100314
 
0.3%
Other values (65971)98694
91.0%

Most occurring characters

ValueCountFrequency (%)
035194
 
8.1%
526267
 
6.1%
424406
 
5.6%
E23584
 
5.4%
121553
 
5.0%
221377
 
4.9%
621282
 
4.9%
318634
 
4.3%
D17287
 
4.0%
716952
 
3.9%
Other values (37)207269
47.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number217490
50.1%
Uppercase Letter216290
49.9%
Other Punctuation8
 
< 0.1%
Lowercase Letter6
 
< 0.1%
Dash Punctuation5
 
< 0.1%
Space Separator2
 
< 0.1%
Modifier Symbol2
 
< 0.1%
Currency Symbol1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E23584
 
10.9%
D17287
 
8.0%
K15337
 
7.1%
B14604
 
6.8%
C13145
 
6.1%
G11001
 
5.1%
F10687
 
4.9%
H10308
 
4.8%
J10267
 
4.7%
A9681
 
4.5%
Other values (14)80389
37.2%
Decimal Number
ValueCountFrequency (%)
035194
16.2%
526267
12.1%
424406
11.2%
121553
9.9%
221377
9.8%
621282
9.8%
318634
8.6%
716952
7.8%
816645
7.7%
915180
7.0%
Lowercase Letter
ValueCountFrequency (%)
b2
33.3%
e2
33.3%
u1
16.7%
n1
16.7%
Other Punctuation
ValueCountFrequency (%)
/5
62.5%
@2
 
25.0%
:1
 
12.5%
Modifier Symbol
ValueCountFrequency (%)
`1
50.0%
^1
50.0%
Dash Punctuation
ValueCountFrequency (%)
-5
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Currency Symbol
ValueCountFrequency (%)
$1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common217509
50.1%
Latin216296
49.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
E23584
 
10.9%
D17287
 
8.0%
K15337
 
7.1%
B14604
 
6.8%
C13145
 
6.1%
G11001
 
5.1%
F10687
 
4.9%
H10308
 
4.8%
J10267
 
4.7%
A9681
 
4.5%
Other values (18)80395
37.2%
Common
ValueCountFrequency (%)
035194
16.2%
526267
12.1%
424406
11.2%
121553
9.9%
221377
9.8%
621282
9.8%
318634
8.6%
716952
7.8%
816645
7.7%
915180
7.0%
Other values (9)19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII433805
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
035194
 
8.1%
526267
 
6.1%
424406
 
5.6%
E23584
 
5.4%
121553
 
5.0%
221377
 
4.9%
621282
 
4.9%
318634
 
4.3%
D17287
 
4.0%
716952
 
3.9%
Other values (37)207269
47.8%

FIRE_NAME
Categorical

HIGH CARDINALITY
MISSING

Distinct194780
Distinct (%)63.3%
Missing318923
Missing (%)50.9%
Memory size4.8 MiB
GRASS FIRE
 
1319
NA
 
1088
UNKNOWN
 
1081
LOCAL
 
703
STATE
 
465
Other values (194775)
303243 

Length

Max length50
Median length44
Mean length11.53998876
Min length1

Characters and Unicode

Total characters3553151
Distinct characters65
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167139 ?
Unique (%)54.3%

Sample

1st rowWEST DODSON
2nd rowTIMBER LAKE
3rd rowTOTAL LOSS (62)
4th rowJONES PLASTIC
5th rowIRONWOOD

Common Values

ValueCountFrequency (%)
GRASS FIRE1319
 
0.2%
NA1088
 
0.2%
UNKNOWN1081
 
0.2%
LOCAL 703
 
0.1%
STATE 465
 
0.1%
LOCAL FIRE 253
 
< 0.1%
COTTONWOOD236
 
< 0.1%
POWERLINE209
 
< 0.1%
WILLOW204
 
< 0.1%
ROCK198
 
< 0.1%
Other values (194770)302143
48.2%
(Missing)318923
50.9%

Length

2022-11-23T17:22:44.314335image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fire21980
 
4.1%
creek9289
 
1.7%
rd8362
 
1.6%
27092
 
1.3%
road6573
 
1.2%
6524
 
1.2%
14060
 
0.8%
lake3260
 
0.6%
hwy3169
 
0.6%
grass3038
 
0.6%
Other values (101101)458244
86.2%

Most occurring characters

ValueCountFrequency (%)
658122
18.5%
E255668
 
7.2%
R223504
 
6.3%
A191369
 
5.4%
O164211
 
4.6%
N151676
 
4.3%
I150188
 
4.2%
L146698
 
4.1%
S127305
 
3.6%
T122990
 
3.5%
Other values (55)1361420
38.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2363153
66.5%
Space Separator658122
 
18.5%
Decimal Number436129
 
12.3%
Dash Punctuation54349
 
1.5%
Other Punctuation31073
 
0.9%
Open Punctuation4829
 
0.1%
Close Punctuation4799
 
0.1%
Connector Punctuation645
 
< 0.1%
Modifier Symbol33
 
< 0.1%
Math Symbol12
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E255668
 
10.8%
R223504
 
9.5%
A191369
 
8.1%
O164211
 
6.9%
N151676
 
6.4%
I150188
 
6.4%
L146698
 
6.2%
S127305
 
5.4%
T122990
 
5.2%
C95857
 
4.1%
Other values (17)733687
31.0%
Other Punctuation
ValueCountFrequency (%)
#10382
33.4%
.9708
31.2%
/5076
16.3%
,1529
 
4.9%
'1464
 
4.7%
&1352
 
4.4%
"1078
 
3.5%
@303
 
1.0%
!56
 
0.2%
?42
 
0.1%
Other values (4)83
 
0.3%
Decimal Number
ValueCountFrequency (%)
0115884
26.6%
180801
18.5%
272597
16.6%
335093
 
8.0%
429145
 
6.7%
527362
 
6.3%
620511
 
4.7%
718377
 
4.2%
818285
 
4.2%
918074
 
4.1%
Open Punctuation
ValueCountFrequency (%)
(4805
99.5%
[15
 
0.3%
{9
 
0.2%
Close Punctuation
ValueCountFrequency (%)
)4781
99.6%
]15
 
0.3%
}3
 
0.1%
Math Symbol
ValueCountFrequency (%)
+8
66.7%
=3
 
25.0%
>1
 
8.3%
Space Separator
ValueCountFrequency (%)
658122
100.0%
Dash Punctuation
ValueCountFrequency (%)
-54349
100.0%
Connector Punctuation
ValueCountFrequency (%)
_645
100.0%
Modifier Symbol
ValueCountFrequency (%)
`33
100.0%
Currency Symbol
ValueCountFrequency (%)
$7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2363153
66.5%
Common1189998
33.5%

Most frequent character per script

Common
ValueCountFrequency (%)
658122
55.3%
0115884
 
9.7%
180801
 
6.8%
272597
 
6.1%
-54349
 
4.6%
335093
 
2.9%
429145
 
2.4%
527362
 
2.3%
620511
 
1.7%
718377
 
1.5%
Other values (28)77757
 
6.5%
Latin
ValueCountFrequency (%)
E255668
 
10.8%
R223504
 
9.5%
A191369
 
8.1%
O164211
 
6.9%
N151676
 
6.4%
I150188
 
6.4%
L146698
 
6.2%
S127305
 
5.4%
T122990
 
5.2%
C95857
 
4.1%
Other values (17)733687
31.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3553146
> 99.9%
None5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
658122
18.5%
E255668
 
7.2%
R223504
 
6.3%
A191369
 
5.4%
O164211
 
4.6%
N151676
 
4.3%
I150188
 
4.2%
L146698
 
4.1%
S127305
 
3.6%
T122990
 
3.5%
Other values (54)1361415
38.3%
None
ValueCountFrequency (%)
Ñ5
100.0%

ICS_209_INCIDENT_NUMBER
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct8000
Distinct (%)92.7%
Missing618192
Missing (%)98.6%
Memory size4.8 MiB
OK-OSA-100020
 
18
CA-MNF-000663
 
16
OR-UPF-009121
 
14
WA-OWF-000583
 
14
ID-PAF-006068
 
13
Other values (7995)
8555 

Length

Max length19
Median length17
Mean length12.10776362
Min length4

Characters and Unicode

Total characters104490
Distinct characters49
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7712 ?
Unique (%)89.4%

Sample

1st rowKY-KYS-08-3003
2nd row702180
3rd rowOK-OKS-20178
4th rowOK-NEU-09-30582
5th rowOR-UPF-009121

Common Values

ValueCountFrequency (%)
OK-OSA-10002018
 
< 0.1%
CA-MNF-00066316
 
< 0.1%
OR-UPF-00912114
 
< 0.1%
WA-OWF-00058314
 
< 0.1%
ID-PAF-00606813
 
< 0.1%
WA-OWF-00055912
 
< 0.1%
CA-SRF-112011
 
< 0.1%
MT-BRF-00013511
 
< 0.1%
WA-MSF-0017711
 
< 0.1%
CA-MDF-00038810
 
< 0.1%
Other values (7990)8500
 
1.4%
(Missing)618192
98.6%

Length

2022-11-23T17:22:44.385903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ok-osa-10002018
 
0.2%
ca-mnf-00066316
 
0.2%
or-upf-00912114
 
0.2%
wa-owf-00058314
 
0.2%
id-paf-00606813
 
0.1%
wa-owf-00055912
 
0.1%
ca-srf-112011
 
0.1%
mt-brf-00013511
 
0.1%
wa-msf-0017711
 
0.1%
ca-mdf-00038810
 
0.1%
Other values (8020)8543
98.5%

Most occurring characters

ValueCountFrequency (%)
-16751
16.0%
015476
 
14.8%
16622
 
6.3%
25292
 
5.1%
S4811
 
4.6%
F3623
 
3.5%
33573
 
3.4%
A3377
 
3.2%
43090
 
3.0%
62968
 
2.8%
Other values (39)38907
37.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number47973
45.9%
Uppercase Letter39371
37.7%
Dash Punctuation16751
 
16.0%
Space Separator376
 
0.4%
Other Punctuation7
 
< 0.1%
Lowercase Letter7
 
< 0.1%
Connector Punctuation3
 
< 0.1%
Currency Symbol1
 
< 0.1%
Modifier Symbol1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S4811
12.2%
F3623
 
9.2%
A3377
 
8.6%
C2686
 
6.8%
N2457
 
6.2%
M2245
 
5.7%
T2206
 
5.6%
D2192
 
5.6%
O2085
 
5.3%
K1807
 
4.6%
Other values (16)11882
30.2%
Decimal Number
ValueCountFrequency (%)
015476
32.3%
16622
13.8%
25292
 
11.0%
33573
 
7.4%
43090
 
6.4%
62968
 
6.2%
52814
 
5.9%
82765
 
5.8%
72727
 
5.7%
92646
 
5.5%
Other Punctuation
ValueCountFrequency (%)
/4
57.1%
:1
 
14.3%
?1
 
14.3%
!1
 
14.3%
Lowercase Letter
ValueCountFrequency (%)
p3
42.9%
y2
28.6%
h1
 
14.3%
s1
 
14.3%
Dash Punctuation
ValueCountFrequency (%)
-16751
100.0%
Space Separator
ValueCountFrequency (%)
376
100.0%
Connector Punctuation
ValueCountFrequency (%)
_3
100.0%
Currency Symbol
ValueCountFrequency (%)
$1
100.0%
Modifier Symbol
ValueCountFrequency (%)
`1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common65112
62.3%
Latin39378
37.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
S4811
12.2%
F3623
 
9.2%
A3377
 
8.6%
C2686
 
6.8%
N2457
 
6.2%
M2245
 
5.7%
T2206
 
5.6%
D2192
 
5.6%
O2085
 
5.3%
K1807
 
4.6%
Other values (20)11889
30.2%
Common
ValueCountFrequency (%)
-16751
25.7%
015476
23.8%
16622
 
10.2%
25292
 
8.1%
33573
 
5.5%
43090
 
4.7%
62968
 
4.6%
52814
 
4.3%
82765
 
4.2%
72727
 
4.2%
Other values (9)3034
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII104490
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
-16751
16.0%
015476
 
14.8%
16622
 
6.3%
25292
 
5.1%
S4811
 
4.6%
F3623
 
3.5%
33573
 
3.4%
A3377
 
3.2%
43090
 
3.0%
62968
 
2.8%
Other values (39)38907
37.2%

ICS_209_NAME
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct7463
Distinct (%)86.5%
Missing618192
Missing (%)98.6%
Memory size4.8 MiB
OSAGE-MIAMI COMPLEX
 
18
Yolla Bolly Complex
 
16
YAKIMA COMPLEX
 
14
Tiller Complex
 
14
South Fork Complex
 
13
Other values (7458)
8555 

Length

Max length37
Median length26
Mean length10.93812283
Min length2

Characters and Unicode

Total characters94396
Distinct characters74
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6809 ?
Unique (%)78.9%

Sample

1st rowHinkle Fire
2nd rowFERGUSON
3rd rowArmadillo
4th rowSellers Mountain
5th rowTiller Complex

Common Values

ValueCountFrequency (%)
OSAGE-MIAMI COMPLEX18
 
< 0.1%
Yolla Bolly Complex16
 
< 0.1%
YAKIMA COMPLEX14
 
< 0.1%
Tiller Complex14
 
< 0.1%
South Fork Complex13
 
< 0.1%
Wenatchee Complex12
 
< 0.1%
Selway-Salmon WFU Complex11
 
< 0.1%
Gold Hill Complex11
 
< 0.1%
Mad Complex11
 
< 0.1%
Muldoon Complex10
 
< 0.1%
Other values (7453)8500
 
1.4%
(Missing)618192
98.6%

Length

2022-11-23T17:22:44.457803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
complex991
 
6.2%
fire775
 
4.8%
creek644
 
4.0%
road277
 
1.7%
lake176
 
1.1%
mountain143
 
0.9%
2136
 
0.8%
fork129
 
0.8%
ridge119
 
0.7%
canyon113
 
0.7%
Other values (5228)12570
78.2%

Most occurring characters

ValueCountFrequency (%)
7561
 
8.0%
e6705
 
7.1%
o4254
 
4.5%
r4042
 
4.3%
a3998
 
4.2%
l3517
 
3.7%
E3386
 
3.6%
C3385
 
3.6%
i3373
 
3.6%
n3261
 
3.5%
Other values (64)50914
53.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter46593
49.4%
Uppercase Letter38108
40.4%
Space Separator7561
 
8.0%
Decimal Number1551
 
1.6%
Other Punctuation351
 
0.4%
Dash Punctuation174
 
0.2%
Open Punctuation29
 
< 0.1%
Close Punctuation29
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e6705
14.4%
o4254
 
9.1%
r4042
 
8.7%
a3998
 
8.6%
l3517
 
7.5%
i3373
 
7.2%
n3261
 
7.0%
t2304
 
4.9%
s1602
 
3.4%
d1507
 
3.2%
Other values (16)12030
25.8%
Uppercase Letter
ValueCountFrequency (%)
E3386
 
8.9%
C3385
 
8.9%
R3199
 
8.4%
L2350
 
6.2%
O2318
 
6.1%
A2312
 
6.1%
S2148
 
5.6%
I1804
 
4.7%
N1782
 
4.7%
M1720
 
4.5%
Other values (16)13704
36.0%
Decimal Number
ValueCountFrequency (%)
2320
20.6%
1287
18.5%
0197
12.7%
3147
9.5%
4131
8.4%
5121
 
7.8%
797
 
6.3%
695
 
6.1%
982
 
5.3%
874
 
4.8%
Other Punctuation
ValueCountFrequency (%)
.135
38.5%
#94
26.8%
/54
 
15.4%
'52
 
14.8%
&12
 
3.4%
@2
 
0.6%
,1
 
0.3%
\1
 
0.3%
Space Separator
ValueCountFrequency (%)
7561
100.0%
Dash Punctuation
ValueCountFrequency (%)
-174
100.0%
Open Punctuation
ValueCountFrequency (%)
(29
100.0%
Close Punctuation
ValueCountFrequency (%)
)29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin84701
89.7%
Common9695
 
10.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e6705
 
7.9%
o4254
 
5.0%
r4042
 
4.8%
a3998
 
4.7%
l3517
 
4.2%
E3386
 
4.0%
C3385
 
4.0%
i3373
 
4.0%
n3261
 
3.9%
R3199
 
3.8%
Other values (42)45581
53.8%
Common
ValueCountFrequency (%)
7561
78.0%
2320
 
3.3%
1287
 
3.0%
0197
 
2.0%
-174
 
1.8%
3147
 
1.5%
.135
 
1.4%
4131
 
1.4%
5121
 
1.2%
797
 
1.0%
Other values (12)525
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII94396
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7561
 
8.0%
e6705
 
7.1%
o4254
 
4.5%
r4042
 
4.3%
a3998
 
4.2%
l3517
 
3.7%
E3386
 
3.6%
C3385
 
3.6%
i3373
 
3.6%
n3261
 
3.5%
Other values (64)50914
53.9%

MTBS_ID
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct3602
Distinct (%)97.6%
Missing623132
Missing (%)99.4%
Memory size4.8 MiB
ID4542411459020120730
 
7
KY3686008359020011102
 
7
ID4568011472320070706
 
4
OR4382211938920070706
 
4
NV3724211430320050622
 
4
Other values (3597)
3664 

Length

Max length29
Median length21
Mean length21.0501355
Min length13

Characters and Unicode

Total characters77675
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3536 ?
Unique (%)95.8%

Sample

1st rowWA4885011944820070818
2nd rowNV3650211498820050622
3rd rowOR4382211938920070706
4th rowWV3788408211820001102
5th rowAK6668614626020150621

Common Values

ValueCountFrequency (%)
ID45424114590201207307
 
< 0.1%
KY36860083590200111027
 
< 0.1%
ID45680114723200707064
 
< 0.1%
OR43822119389200707064
 
< 0.1%
NV37242114303200506224
 
< 0.1%
MT45746107166201207313
 
< 0.1%
CA39852121444200808143
 
< 0.1%
NV40703116067200707173
 
< 0.1%
CA40642123586201507313
 
< 0.1%
NV40116117067200707163
 
< 0.1%
Other values (3592)3649
 
0.6%
(Missing)623132
99.4%

Length

2022-11-23T17:22:44.529919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
id45424114590201207307
 
0.2%
ky36860083590200111027
 
0.2%
id45680114723200707064
 
0.1%
or43822119389200707064
 
0.1%
nv37242114303200506224
 
0.1%
nv40116117067200707163
 
0.1%
tx32097101167201102273
 
0.1%
ca40642123586201507313
 
0.1%
nv40703116067200707173
 
0.1%
ca39852121444200808143
 
0.1%
Other values (3592)3649
98.9%

Most occurring characters

ValueCountFrequency (%)
014578
18.8%
111411
14.7%
28310
10.7%
35723
 
7.4%
95704
 
7.3%
45585
 
7.2%
84765
 
6.1%
64549
 
5.9%
54301
 
5.5%
74199
 
5.4%
Other values (27)8550
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number69125
89.0%
Uppercase Letter8080
 
10.4%
Dash Punctuation470
 
0.6%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A1172
14.5%
N651
 
8.1%
T646
 
8.0%
M567
 
7.0%
K520
 
6.4%
C516
 
6.4%
D494
 
6.1%
O442
 
5.5%
I423
 
5.2%
V342
 
4.2%
Other values (16)2307
28.6%
Decimal Number
ValueCountFrequency (%)
014578
21.1%
111411
16.5%
28310
12.0%
35723
 
8.3%
95704
 
8.3%
45585
 
8.1%
84765
 
6.9%
64549
 
6.6%
54301
 
6.2%
74199
 
6.1%
Dash Punctuation
ValueCountFrequency (%)
-470
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common69595
89.6%
Latin8080
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A1172
14.5%
N651
 
8.1%
T646
 
8.0%
M567
 
7.0%
K520
 
6.4%
C516
 
6.4%
D494
 
6.1%
O442
 
5.5%
I423
 
5.2%
V342
 
4.2%
Other values (16)2307
28.6%
Common
ValueCountFrequency (%)
014578
20.9%
111411
16.4%
28310
11.9%
35723
 
8.2%
95704
 
8.2%
45585
 
8.0%
84765
 
6.8%
64549
 
6.5%
54301
 
6.2%
74199
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII77675
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
014578
18.8%
111411
14.7%
28310
10.7%
35723
 
7.4%
95704
 
7.3%
45585
 
7.2%
84765
 
6.1%
64549
 
5.9%
54301
 
5.5%
74199
 
5.4%
Other values (27)8550
11.0%

MTBS_FIRE_NAME
Categorical

HIGH CARDINALITY
MISSING

Distinct3075
Distinct (%)83.3%
Missing623132
Missing (%)99.4%
Memory size4.8 MiB
UNNAMED
 
259
MUSTANG COMPLEX
 
7
COTTONWOOD
 
6
CANYON
 
6
CAMP CREEK
 
6
Other values (3070)
3406 

Length

Max length49
Median length42
Mean length10.55474255
Min length2

Characters and Unicode

Total characters38947
Distinct characters48
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2820 ?
Unique (%)76.4%

Sample

1st rowCASCADE COMPLEX (WHISKEY)
2nd rowSOUTHERN NEVADA COMPLEX (COYOTE)
3rd rowEGLEY COMPLEX (EGLEY)
4th rowBLAIR FORK
5th rowCRAZY SLOUGH

Common Values

ValueCountFrequency (%)
UNNAMED259
 
< 0.1%
MUSTANG COMPLEX7
 
< 0.1%
COTTONWOOD6
 
< 0.1%
CANYON6
 
< 0.1%
CAMP CREEK6
 
< 0.1%
RATTLESNAKE6
 
< 0.1%
WILLOW6
 
< 0.1%
WILDCAT5
 
< 0.1%
WINDMILL5
 
< 0.1%
EGLEY COMPLEX (EGLEY)4
 
< 0.1%
Other values (3065)3380
 
0.5%
(Missing)623132
99.4%

Length

2022-11-23T17:22:44.596669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
complex318
 
5.0%
creek269
 
4.3%
unnamed259
 
4.1%
fire89
 
1.4%
282
 
1.3%
lake66
 
1.0%
canyon65
 
1.0%
river62
 
1.0%
mountain57
 
0.9%
fork51
 
0.8%
Other values (2713)4984
79.1%

Most occurring characters

ValueCountFrequency (%)
E4068
 
10.4%
A2845
 
7.3%
R2668
 
6.9%
N2651
 
6.8%
2618
 
6.7%
O2571
 
6.6%
L2242
 
5.8%
I1987
 
5.1%
C1745
 
4.5%
T1652
 
4.2%
Other values (38)13900
35.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter35110
90.1%
Space Separator2618
 
6.7%
Decimal Number618
 
1.6%
Close Punctuation217
 
0.6%
Open Punctuation217
 
0.6%
Other Punctuation98
 
0.3%
Dash Punctuation66
 
0.2%
Lowercase Letter3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E4068
 
11.6%
A2845
 
8.1%
R2668
 
7.6%
N2651
 
7.6%
O2571
 
7.3%
L2242
 
6.4%
I1987
 
5.7%
C1745
 
5.0%
T1652
 
4.7%
S1555
 
4.4%
Other values (16)11126
31.7%
Decimal Number
ValueCountFrequency (%)
2150
24.3%
1106
17.2%
071
11.5%
364
10.4%
449
 
7.9%
546
 
7.4%
739
 
6.3%
638
 
6.1%
928
 
4.5%
827
 
4.4%
Other Punctuation
ValueCountFrequency (%)
#44
44.9%
.25
25.5%
'16
 
16.3%
/12
 
12.2%
&1
 
1.0%
Lowercase Letter
ValueCountFrequency (%)
a1
33.3%
n1
33.3%
d1
33.3%
Space Separator
ValueCountFrequency (%)
2618
100.0%
Close Punctuation
ValueCountFrequency (%)
)217
100.0%
Open Punctuation
ValueCountFrequency (%)
(217
100.0%
Dash Punctuation
ValueCountFrequency (%)
-66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin35113
90.2%
Common3834
 
9.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E4068
 
11.6%
A2845
 
8.1%
R2668
 
7.6%
N2651
 
7.5%
O2571
 
7.3%
L2242
 
6.4%
I1987
 
5.7%
C1745
 
5.0%
T1652
 
4.7%
S1555
 
4.4%
Other values (19)11129
31.7%
Common
ValueCountFrequency (%)
2618
68.3%
)217
 
5.7%
(217
 
5.7%
2150
 
3.9%
1106
 
2.8%
071
 
1.9%
-66
 
1.7%
364
 
1.7%
449
 
1.3%
546
 
1.2%
Other values (9)230
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII38947
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E4068
 
10.4%
A2845
 
7.3%
R2668
 
6.9%
N2651
 
6.8%
2618
 
6.7%
O2571
 
6.6%
L2242
 
5.8%
I1987
 
5.1%
C1745
 
4.5%
T1652
 
4.2%
Other values (38)13900
35.7%

COMPLEX_NAME
Categorical

HIGH CARDINALITY
MISSING

Distinct824
Distinct (%)47.2%
Missing625075
Missing (%)99.7%
Memory size4.8 MiB
TILLER COMPLEX
 
20
OSAGE-MIAMI COMPLEX
 
18
YOLLA BOLLY COMPLEX 2008
 
16
SOUTH FORK COMPLEX
 
15
VALLEY COMPLEX
 
15
Other values (819)
1663 

Length

Max length43
Median length33
Mean length17.63594734
Min length7

Characters and Unicode

Total characters30810
Distinct characters46
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique478 ?
Unique (%)27.4%

Sample

1st rowCASCADE COMPLEX
2nd rowTILLER COMPLEX
3rd rowCARRIZO COMPLEX
4th rowYAAK-RED DRAGON COMPLEX
5th rowSOUTHERN NEVADA COMPLEX

Common Values

ValueCountFrequency (%)
TILLER COMPLEX20
 
< 0.1%
OSAGE-MIAMI COMPLEX18
 
< 0.1%
YOLLA BOLLY COMPLEX 200816
 
< 0.1%
SOUTH FORK COMPLEX15
 
< 0.1%
VALLEY COMPLEX15
 
< 0.1%
YAKIMA COMPLEX14
 
< 0.1%
WENATCHEE RIVER COMPLEX13
 
< 0.1%
SELWAY-SALMON WFU COMPLEX11
 
< 0.1%
MAD COMPLEX11
 
< 0.1%
MOTORWAY COMPLEX11
 
< 0.1%
Other values (814)1603
 
0.3%
(Missing)625075
99.7%

Length

2022-11-23T17:22:44.786898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
complex1731
38.3%
creek72
 
1.6%
lake64
 
1.4%
lightning61
 
1.3%
river45
 
1.0%
wfu38
 
0.8%
fork38
 
0.8%
south35
 
0.8%
fire32
 
0.7%
red29
 
0.6%
Other values (873)2378
52.6%

Most occurring characters

ValueCountFrequency (%)
E3456
11.2%
L2884
 
9.4%
O2820
 
9.2%
2756
 
8.9%
C2306
 
7.5%
M2203
 
7.2%
P2022
 
6.6%
X1764
 
5.7%
A1328
 
4.3%
R1192
 
3.9%
Other values (36)8079
26.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter27525
89.3%
Space Separator2793
 
9.1%
Decimal Number324
 
1.1%
Dash Punctuation86
 
0.3%
Other Punctuation70
 
0.2%
Open Punctuation6
 
< 0.1%
Close Punctuation6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E3456
12.6%
L2884
10.5%
O2820
10.2%
C2306
 
8.4%
M2203
 
8.0%
P2022
 
7.3%
X1764
 
6.4%
A1328
 
4.8%
R1192
 
4.3%
I965
 
3.5%
Other values (16)6585
23.9%
Decimal Number
ValueCountFrequency (%)
0101
31.2%
262
19.1%
128
 
8.6%
727
 
8.3%
827
 
8.3%
323
 
7.1%
517
 
5.2%
415
 
4.6%
914
 
4.3%
610
 
3.1%
Other Punctuation
ValueCountFrequency (%)
/36
51.4%
.21
30.0%
&6
 
8.6%
'5
 
7.1%
#2
 
2.9%
Space Separator
ValueCountFrequency (%)
2756
98.7%
 37
 
1.3%
Dash Punctuation
ValueCountFrequency (%)
-86
100.0%
Open Punctuation
ValueCountFrequency (%)
(6
100.0%
Close Punctuation
ValueCountFrequency (%)
)6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin27525
89.3%
Common3285
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E3456
12.6%
L2884
10.5%
O2820
10.2%
C2306
 
8.4%
M2203
 
8.0%
P2022
 
7.3%
X1764
 
6.4%
A1328
 
4.8%
R1192
 
4.3%
I965
 
3.5%
Other values (16)6585
23.9%
Common
ValueCountFrequency (%)
2756
83.9%
0101
 
3.1%
-86
 
2.6%
262
 
1.9%
 37
 
1.1%
/36
 
1.1%
128
 
0.9%
727
 
0.8%
827
 
0.8%
323
 
0.7%
Other values (10)102
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII30773
99.9%
None37
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E3456
11.2%
L2884
 
9.4%
O2820
 
9.2%
2756
 
9.0%
C2306
 
7.5%
M2203
 
7.2%
P2022
 
6.6%
X1764
 
5.7%
A1328
 
4.3%
R1192
 
3.9%
Other values (35)8042
26.1%
None
ValueCountFrequency (%)
 37
100.0%

FIRE_YEAR
Real number (ℝ≥0)

HIGH CORRELATION

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.707373
Minimum1992
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2022-11-23T17:22:44.842567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1992
5-th percentile1993
Q11998
median2004
Q32009
95-th percentile2014
Maximum2015
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.659931294
Coefficient of variation (CV)0.003323804356
Kurtosis-1.11283387
Mean2003.707373
Median Absolute Deviation (MAD)5
Skewness-0.05660814944
Sum1255967863
Variance44.35468484
MonotonicityNot monotonic
2022-11-23T17:22:44.895291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
200637962
 
6.1%
200032426
 
5.2%
200731944
 
5.1%
201130106
 
4.8%
199929874
 
4.8%
200529485
 
4.7%
200128932
 
4.6%
200828515
 
4.5%
201026786
 
4.3%
200926111
 
4.2%
Other values (14)324681
51.8%
ValueCountFrequency (%)
199222642
3.6%
199320662
3.3%
199425226
4.0%
199523694
3.8%
199625314
4.0%
199720554
3.3%
199822733
3.6%
199929874
4.8%
200032426
5.2%
200128932
4.6%
ValueCountFrequency (%)
201524758
3.9%
201422398
3.6%
201321687
3.5%
201224215
3.9%
201130106
4.8%
201026786
4.3%
200926111
4.2%
200828515
4.5%
200731944
5.1%
200637962
6.1%

DISCOVERY_DATE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct8765
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2453062.707
Minimum2448622.5
Maximum2457387.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2022-11-23T17:22:44.954157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2448622.5
5-th percentile2449148.5
Q12451085.5
median2453174.5
Q32455035.5
95-th percentile2456866.5
Maximum2457387.5
Range8765
Interquartile range (IQR)3950

Descriptive statistics

Standard deviation2433.509948
Coefficient of variation (CV)0.0009920292461
Kurtosis-1.105494809
Mean2453062.707
Median Absolute Deviation (MAD)1960
Skewness-0.05722575243
Sum1.537633672 × 1012
Variance5921970.668
MonotonicityNot monotonic
2022-11-23T17:22:45.011895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2454506.5404
 
0.1%
2455611.5394
 
0.1%
2453441.5374
 
0.1%
2453798.5373
 
0.1%
2453476.5340
 
0.1%
2453799.5336
 
0.1%
2449773.5333
 
0.1%
2449430.5330
 
0.1%
2456112.5330
 
0.1%
2449056.5323
 
0.1%
Other values (8755)623285
99.4%
ValueCountFrequency (%)
2448622.546
< 0.1%
2448623.516
 
< 0.1%
2448624.517
 
< 0.1%
2448625.519
 
< 0.1%
2448626.518
 
< 0.1%
2448627.529
< 0.1%
2448628.550
< 0.1%
2448629.528
< 0.1%
2448630.511
 
< 0.1%
2448631.527
< 0.1%
ValueCountFrequency (%)
2457387.510
< 0.1%
2457386.59
< 0.1%
2457385.59
< 0.1%
2457384.53
 
< 0.1%
2457383.53
 
< 0.1%
2457382.514
< 0.1%
2457381.53
 
< 0.1%
2457380.510
< 0.1%
2457379.515
< 0.1%
2457378.519
< 0.1%

DISCOVERY_DOY
Real number (ℝ≥0)

HIGH CORRELATION

Distinct366
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.7199971
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2022-11-23T17:22:45.073558image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile31
Q189
median164
Q3230
95-th percentile322
Maximum366
Range365
Interquartile range (IQR)141

Descriptive statistics

Standard deviation89.99580704
Coefficient of variation (CV)0.5463562933
Kurtosis-0.8879149857
Mean164.7199971
Median Absolute Deviation (MAD)71
Skewness0.2257268019
Sum103250118
Variance8099.245284
MonotonicityNot monotonic
2022-11-23T17:22:45.132676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1854196
 
0.7%
1863921
 
0.6%
1083176
 
0.5%
1013114
 
0.5%
833074
 
0.5%
1093028
 
0.5%
673017
 
0.5%
1003017
 
0.5%
922948
 
0.5%
952938
 
0.5%
Other values (356)594393
94.8%
ValueCountFrequency (%)
11338
0.2%
2906
0.1%
3836
0.1%
4813
0.1%
5920
0.1%
6826
0.1%
71007
0.2%
8931
0.1%
9806
0.1%
10765
0.1%
ValueCountFrequency (%)
366176
 
< 0.1%
365970
0.2%
364695
0.1%
363712
0.1%
362793
0.1%
361814
0.1%
360699
0.1%
359411
0.1%
358532
0.1%
357600
0.1%

DISCOVERY_TIME
Real number (ℝ≥0)

MISSING

Distinct1440
Distinct (%)0.4%
Missing294076
Missing (%)46.9%
Infinite0
Infinite (%)0.0%
Mean1452.309356
Minimum0
Maximum2359
Zeros235
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2022-11-23T17:22:45.195641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile740
Q11240
median1457
Q31708
95-th percentile2100
Maximum2359
Range2359
Interquartile range (IQR)468

Descriptive statistics

Standard deviation406.5786483
Coefficient of variation (CV)0.2799531977
Kurtosis1.445735304
Mean1452.309356
Median Absolute Deviation (MAD)234
Skewness-0.7034758649
Sum483250129
Variance165306.1973
MonotonicityNot monotonic
2022-11-23T17:22:45.255921image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14007016
 
1.1%
15006747
 
1.1%
16005976
 
1.0%
13005801
 
0.9%
17004704
 
0.8%
12004642
 
0.7%
15304395
 
0.7%
14304302
 
0.7%
18003988
 
0.6%
16303893
 
0.6%
Other values (1430)281282
44.9%
(Missing)294076
46.9%
ValueCountFrequency (%)
0235
< 0.1%
1266
< 0.1%
237
 
< 0.1%
327
 
< 0.1%
428
 
< 0.1%
590
 
< 0.1%
621
 
< 0.1%
720
 
< 0.1%
831
 
< 0.1%
940
 
< 0.1%
ValueCountFrequency (%)
235995
< 0.1%
235838
 
< 0.1%
235742
< 0.1%
235632
 
< 0.1%
235563
< 0.1%
235433
 
< 0.1%
235332
 
< 0.1%
235234
 
< 0.1%
235120
 
< 0.1%
235099
< 0.1%

STAT_CAUSE_CODE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.974547798
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2022-11-23T17:22:45.304961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile13
Maximum13
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.484163051
Coefficient of variation (CV)0.5831676586
Kurtosis-0.6034509812
Mean5.974547798
Median Absolute Deviation (MAD)3
Skewness0.311491501
Sum3744978
Variance12.13939216
MonotonicityNot monotonic
2022-11-23T17:22:45.353011image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
5143074
22.8%
9108372
17.3%
793304
14.9%
193057
14.8%
1355397
 
8.8%
249423
 
7.9%
425367
 
4.0%
820354
 
3.2%
317571
 
2.8%
611053
 
1.8%
Other values (3)9850
 
1.6%
ValueCountFrequency (%)
193057
14.8%
249423
 
7.9%
317571
 
2.8%
425367
 
4.0%
5143074
22.8%
611053
 
1.8%
793304
14.9%
820354
 
3.2%
9108372
17.3%
103865
 
0.6%
ValueCountFrequency (%)
1355397
 
8.8%
121252
 
0.2%
114733
 
0.8%
103865
 
0.6%
9108372
17.3%
820354
 
3.2%
793304
14.9%
611053
 
1.8%
5143074
22.8%
425367
 
4.0%

STAT_CAUSE_DESCR
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
Debris Burning
143074 
Miscellaneous
108372 
Arson
93304 
Lightning
93057 
Missing/Undefined
55397 
Other values (8)
133618 

Length

Max length17
Median length13
Mean length11.11319003
Min length5

Characters and Unicode

Total characters6965992
Distinct characters35
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMissing/Undefined
2nd rowMiscellaneous
3rd rowMiscellaneous
4th rowMiscellaneous
5th rowArson

Common Values

ValueCountFrequency (%)
Debris Burning143074
22.8%
Miscellaneous108372
17.3%
Arson93304
14.9%
Lightning93057
14.8%
Missing/Undefined55397
 
8.8%
Equipment Use49423
 
7.9%
Campfire25367
 
4.0%
Children20354
 
3.2%
Smoking17571
 
2.8%
Railroad11053
 
1.8%
Other values (3)9850
 
1.6%

Length

2022-11-23T17:22:45.405166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
debris143074
17.5%
burning143074
17.5%
miscellaneous108372
13.2%
arson93304
11.4%
lightning93057
11.4%
missing/undefined55397
 
6.8%
equipment49423
 
6.0%
use49423
 
6.0%
campfire25367
 
3.1%
children20354
 
2.5%
Other values (5)38474
 
4.7%

Most occurring characters

ValueCountFrequency (%)
n932210
13.4%
i879191
12.6%
e629762
 
9.0%
s617204
 
8.9%
r451193
 
6.5%
g402156
 
5.8%
u303373
 
4.4%
l252884
 
3.6%
o238898
 
3.4%
192497
 
2.8%
Other values (25)2066624
29.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter5843382
83.9%
Uppercase Letter874716
 
12.6%
Space Separator192497
 
2.8%
Other Punctuation55397
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n932210
16.0%
i879191
15.0%
e629762
10.8%
s617204
10.6%
r451193
7.7%
g402156
6.9%
u303373
 
5.2%
l252884
 
4.3%
o238898
 
4.1%
a155845
 
2.7%
Other values (11)980666
16.8%
Uppercase Letter
ValueCountFrequency (%)
M163769
18.7%
D143074
16.4%
B143074
16.4%
U104820
12.0%
A93304
10.7%
L93057
10.6%
E49423
 
5.7%
C45721
 
5.2%
S18823
 
2.2%
R11053
 
1.3%
Other values (2)8598
 
1.0%
Space Separator
ValueCountFrequency (%)
192497
100.0%
Other Punctuation
ValueCountFrequency (%)
/55397
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin6718098
96.4%
Common247894
 
3.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
n932210
13.9%
i879191
13.1%
e629762
 
9.4%
s617204
 
9.2%
r451193
 
6.7%
g402156
 
6.0%
u303373
 
4.5%
l252884
 
3.8%
o238898
 
3.6%
M163769
 
2.4%
Other values (23)1847458
27.5%
Common
ValueCountFrequency (%)
192497
77.7%
/55397
 
22.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII6965992
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n932210
13.4%
i879191
12.6%
e629762
 
9.0%
s617204
 
8.9%
r451193
 
6.5%
g402156
 
5.8%
u303373
 
4.4%
l252884
 
3.6%
o238898
 
3.4%
192497
 
2.8%
Other values (25)2066624
29.7%

CONT_DATE
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct8723
Distinct (%)2.6%
Missing297088
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean2453239.748
Minimum2448622.5
Maximum2457388.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2022-11-23T17:22:45.464583image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2448622.5
5-th percentile2449060.5
Q12450707.5
median2453470.5
Q32455749.5
95-th percentile2457108.5
Maximum2457388.5
Range8766
Interquartile range (IQR)5042

Descriptive statistics

Standard deviation2684.869396
Coefficient of variation (CV)0.001094417861
Kurtosis-1.32179657
Mean2453239.748
Median Absolute Deviation (MAD)2413
Skewness-0.1229856739
Sum8.08916555 × 1011
Variance7208523.676
MonotonicityNot monotonic
2022-11-23T17:22:45.524379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2455611.5300
 
< 0.1%
2457067.5238
 
< 0.1%
2450309.5225
 
< 0.1%
2449056.5222
 
< 0.1%
2448683.5215
 
< 0.1%
2450137.5206
 
< 0.1%
2455606.5204
 
< 0.1%
2456367.5202
 
< 0.1%
2456112.5194
 
< 0.1%
2453476.5192
 
< 0.1%
Other values (8713)327536
52.3%
(Missing)297088
47.4%
ValueCountFrequency (%)
2448622.521
< 0.1%
2448623.58
 
< 0.1%
2448624.59
 
< 0.1%
2448625.513
 
< 0.1%
2448626.512
 
< 0.1%
2448627.522
< 0.1%
2448628.540
< 0.1%
2448629.516
 
< 0.1%
2448630.55
 
< 0.1%
2448631.514
 
< 0.1%
ValueCountFrequency (%)
2457388.51
 
< 0.1%
2457387.59
< 0.1%
2457386.57
< 0.1%
2457385.510
< 0.1%
2457384.54
 
< 0.1%
2457383.53
 
< 0.1%
2457382.58
< 0.1%
2457381.53
 
< 0.1%
2457380.59
< 0.1%
2457379.58
< 0.1%

CONT_DOY
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct366
Distinct (%)0.1%
Missing297088
Missing (%)47.4%
Infinite0
Infinite (%)0.0%
Mean172.7999236
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2022-11-23T17:22:45.589683image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile39
Q1103
median181
Q3232
95-th percentile316
Maximum366
Range365
Interquartile range (IQR)129

Descriptive statistics

Standard deviation84.243639
Coefficient of variation (CV)0.4875212747
Kurtosis-0.7964621105
Mean172.7999236
Median Absolute Deviation (MAD)65
Skewness0.06271418981
Sum56978010
Variance7096.990712
MonotonicityNot monotonic
2022-11-23T17:22:45.656238image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1862407
 
0.4%
1852372
 
0.4%
2171827
 
0.3%
2051777
 
0.3%
1841774
 
0.3%
2161769
 
0.3%
1871768
 
0.3%
2041743
 
0.3%
2201740
 
0.3%
2061729
 
0.3%
Other values (356)310828
49.6%
(Missing)297088
47.4%
ValueCountFrequency (%)
1374
0.1%
2255
< 0.1%
3287
< 0.1%
4283
< 0.1%
5329
0.1%
6346
0.1%
7371
0.1%
8314
0.1%
9318
0.1%
10303
< 0.1%
ValueCountFrequency (%)
36652
 
< 0.1%
365270
< 0.1%
364213
< 0.1%
363249
< 0.1%
362303
< 0.1%
361279
< 0.1%
360250
< 0.1%
359164
< 0.1%
358205
< 0.1%
357235
< 0.1%

CONT_TIME
Categorical

HIGH CARDINALITY
MISSING

Distinct1441
Distinct (%)0.5%
Missing323724
Missing (%)51.6%
Memory size4.8 MiB
1800
 
12786
1600
 
7357
1700
 
6871
1200
 
6413
1500
 
6307
Other values (1436)
263364 

Length

Max length4
Median length4
Mean length3.998205201
Min length0

Characters and Unicode

Total characters1211848
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1800
2nd row1630
3rd row2045
4th row1725
5th row1600

Common Values

ValueCountFrequency (%)
180012786
 
2.0%
16007357
 
1.2%
17006871
 
1.1%
12006413
 
1.0%
15006307
 
1.0%
20005815
 
0.9%
14005353
 
0.9%
19005086
 
0.8%
16304794
 
0.8%
13004423
 
0.7%
Other values (1431)237893
38.0%
(Missing)323724
51.6%

Length

2022-11-23T17:22:45.719910image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
180012786
 
4.2%
16007357
 
2.4%
17006871
 
2.3%
12006413
 
2.1%
15006307
 
2.1%
20005815
 
1.9%
14005353
 
1.8%
19005086
 
1.7%
16304794
 
1.6%
13004423
 
1.5%
Other values (1430)237757
78.5%

Most occurring characters

ValueCountFrequency (%)
1313648
25.9%
0310010
25.6%
2116834
 
9.6%
5114866
 
9.5%
3105646
 
8.7%
480264
 
6.6%
846706
 
3.9%
646220
 
3.8%
741935
 
3.5%
935719
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1211848
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1313648
25.9%
0310010
25.6%
2116834
 
9.6%
5114866
 
9.5%
3105646
 
8.7%
480264
 
6.6%
846706
 
3.9%
646220
 
3.8%
741935
 
3.5%
935719
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common1211848
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1313648
25.9%
0310010
25.6%
2116834
 
9.6%
5114866
 
9.5%
3105646
 
8.7%
480264
 
6.6%
846706
 
3.9%
646220
 
3.8%
741935
 
3.5%
935719
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII1211848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1313648
25.9%
0310010
25.6%
2116834
 
9.6%
5114866
 
9.5%
3105646
 
8.7%
480264
 
6.6%
846706
 
3.9%
646220
 
3.8%
741935
 
3.5%
935719
 
2.9%

FIRE_SIZE
Real number (ℝ≥0)

SKEWED

Distinct7368
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.00601511
Minimum1 × 10-5
Maximum537627
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2022-11-23T17:22:45.781433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1 × 10-5
5-th percentile0.1
Q10.1
median1
Q33.27
95-th percentile46
Maximum537627
Range537627
Interquartile range (IQR)3.17

Descriptive statistics

Standard deviation2169.51733
Coefficient of variation (CV)30.9904417
Kurtosis16279.05137
Mean70.00601511
Median Absolute Deviation (MAD)0.9
Skewness103.4342329
Sum43881310.4
Variance4706805.445
MonotonicityNot monotonic
2022-11-23T17:22:45.847916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1153535
24.5%
174031
 
11.8%
0.537829
 
6.0%
236562
 
5.8%
0.225064
 
4.0%
321927
 
3.5%
520693
 
3.3%
0.2517864
 
2.8%
0.317599
 
2.8%
412691
 
2.0%
Other values (7358)209027
33.3%
ValueCountFrequency (%)
1 × 10-51
 
< 0.1%
0.00013
 
< 0.1%
0.000221
 
< 0.1%
0.00138
< 0.1%
0.001591
 
< 0.1%
0.0028
 
< 0.1%
0.00313
 
< 0.1%
0.0045
 
< 0.1%
0.0055
 
< 0.1%
0.0062
 
< 0.1%
ValueCountFrequency (%)
5376271
< 0.1%
4999451
< 0.1%
3144441
< 0.1%
2978451
< 0.1%
2831801
< 0.1%
2759601
< 0.1%
2575491
< 0.1%
2483101
< 0.1%
2439001
< 0.1%
238462.61
< 0.1%

FIRE_SIZE_CLASS
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
B
313025 
A
222359 
C
73185 
D
 
9604
E
 
4763
Other values (2)
 
3886

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters626822
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowA
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B313025
49.9%
A222359
35.5%
C73185
 
11.7%
D9604
 
1.5%
E4763
 
0.8%
F2651
 
0.4%
G1235
 
0.2%

Length

2022-11-23T17:22:45.905535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-11-23T17:22:45.960702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
b313025
49.9%
a222359
35.5%
c73185
 
11.7%
d9604
 
1.5%
e4763
 
0.8%
f2651
 
0.4%
g1235
 
0.2%

Most occurring characters

ValueCountFrequency (%)
B313025
49.9%
A222359
35.5%
C73185
 
11.7%
D9604
 
1.5%
E4763
 
0.8%
F2651
 
0.4%
G1235
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter626822
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B313025
49.9%
A222359
35.5%
C73185
 
11.7%
D9604
 
1.5%
E4763
 
0.8%
F2651
 
0.4%
G1235
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin626822
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B313025
49.9%
A222359
35.5%
C73185
 
11.7%
D9604
 
1.5%
E4763
 
0.8%
F2651
 
0.4%
G1235
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII626822
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B313025
49.9%
A222359
35.5%
C73185
 
11.7%
D9604
 
1.5%
E4763
 
0.8%
F2651
 
0.4%
G1235
 
0.2%

LATITUDE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct367949
Distinct (%)58.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.77900233
Minimum17.93972222
Maximum70.1381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2022-11-23T17:22:46.148180image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum17.93972222
5-th percentile29.20650833
Q132.8153188
median35.455
Q340.831903
95-th percentile47.19416667
Maximum70.1381
Range52.19837778
Interquartile range (IQR)8.016584202

Descriptive statistics

Standard deviation6.141014462
Coefficient of variation (CV)0.1669706646
Kurtosis1.890375844
Mean36.77900233
Median Absolute Deviation (MAD)3.546105555
Skewness0.4787786134
Sum23053887.8
Variance37.71205863
MonotonicityNot monotonic
2022-11-23T17:22:46.213672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.8666322
 
0.1%
33.3353222
 
< 0.1%
33.3517205
 
< 0.1%
17.970539197
 
< 0.1%
47.8833194
 
< 0.1%
35.3185
 
< 0.1%
33.3167171
 
< 0.1%
41.0665165
 
< 0.1%
34.01194444160
 
< 0.1%
35.6833156
 
< 0.1%
Other values (367939)624845
99.7%
ValueCountFrequency (%)
17.939722221
 
< 0.1%
17.9449241
 
< 0.1%
17.951941
 
< 0.1%
17.9538891
 
< 0.1%
17.95653354
< 0.1%
17.9566671
 
< 0.1%
17.9578361
 
< 0.1%
17.9579071
 
< 0.1%
17.958364103
< 0.1%
17.958386
 
< 0.1%
ValueCountFrequency (%)
70.13811
< 0.1%
69.777451
< 0.1%
69.61891
< 0.1%
69.4331
< 0.1%
69.33671
< 0.1%
69.26441
< 0.1%
69.23221
< 0.1%
69.23141
< 0.1%
69.21611
< 0.1%
69.18171
< 0.1%

LONGITUDE
Real number (ℝ)

HIGH CORRELATION

Distinct408890
Distinct (%)65.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-95.72407666
Minimum-178.8026
Maximum-65.264175
Zeros0
Zeros (%)0.0%
Negative626822
Negative (%)100.0%
Memory size4.8 MiB
2022-11-23T17:22:46.282332image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-178.8026
5-th percentile-122.0380555
Q1-110.4277373
median-92.07974501
Q3-82.3003075
95-th percentile-74.2
Maximum-65.264175
Range113.538425
Interquartile range (IQR)28.12742979

Descriptive statistics

Standard deviation16.72627051
Coefficient of variation (CV)-0.1747342057
Kurtosis0.1337491017
Mean-95.72407666
Median Absolute Deviation (MAD)11.02446168
Skewness-0.7139231522
Sum-60001957.18
Variance279.7681251
MonotonicityNot monotonic
2022-11-23T17:22:46.345248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-110.4518278
 
< 0.1%
-123.6845247
 
< 0.1%
-66.246414197
 
< 0.1%
-110.4507177
 
< 0.1%
-123.6678119
 
< 0.1%
-116.8347118
 
< 0.1%
-66.386131118
 
< 0.1%
-94.9003116
 
< 0.1%
-110.4573115
 
< 0.1%
-81.9113
 
< 0.1%
Other values (408880)625224
99.7%
ValueCountFrequency (%)
-178.80261
< 0.1%
-173.38571
< 0.1%
-166.86941
< 0.1%
-166.15271
< 0.1%
-166.151
< 0.1%
-166.02941
< 0.1%
-165.85271
< 0.1%
-165.58571
< 0.1%
-165.5691
< 0.1%
-165.40231
< 0.1%
ValueCountFrequency (%)
-65.2641752
 
< 0.1%
-65.275555561
 
< 0.1%
-65.285833331
 
< 0.1%
-65.28751
 
< 0.1%
-65.2880671
 
< 0.1%
-65.28831
 
< 0.1%
-65.291111111
 
< 0.1%
-65.3085566
< 0.1%
-65.314444441
 
< 0.1%
-65.321
 
< 0.1%

OWNER_CODE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.59919722
Minimum0
Maximum15
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2022-11-23T17:22:46.400720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median14
Q314
95-th percentile14
Maximum15
Range15
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.402621208
Coefficient of variation (CV)0.4153730813
Kurtosis-0.8051308236
Mean10.59919722
Median Absolute Deviation (MAD)0
Skewness-0.8278496349
Sum6643810
Variance19.3830735
MonotonicityNot monotonic
2022-11-23T17:22:46.446159image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
14350345
55.9%
8104670
 
16.7%
563043
 
10.1%
235396
 
5.6%
1324009
 
3.8%
121074
 
3.4%
710485
 
1.7%
35731
 
0.9%
44116
 
0.7%
92948
 
0.5%
Other values (6)5005
 
0.8%
ValueCountFrequency (%)
06
 
< 0.1%
121074
 
3.4%
235396
 
5.6%
35731
 
0.9%
44116
 
0.7%
563043
10.1%
62107
 
0.3%
710485
 
1.7%
8104670
16.7%
92948
 
0.5%
ValueCountFrequency (%)
15767
 
0.1%
14350345
55.9%
1324009
 
3.8%
121420
 
0.2%
11601
 
0.1%
10104
 
< 0.1%
92948
 
0.5%
8104670
 
16.7%
710485
 
1.7%
62107
 
0.3%

OWNER_DESCR
Categorical

HIGH CORRELATION

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
MISSING/NOT SPECIFIED
350345 
PRIVATE
104670 
USFS
63043 
BIA
35396 
STATE OR PRIVATE
 
24009
Other values (11)
49359 

Length

Max length21
Median length21
Mean length14.45546742
Min length3

Characters and Unicode

Total characters9061005
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMISSING/NOT SPECIFIED
2nd rowPRIVATE
3rd rowMISSING/NOT SPECIFIED
4th rowMISSING/NOT SPECIFIED
5th rowMISSING/NOT SPECIFIED

Common Values

ValueCountFrequency (%)
MISSING/NOT SPECIFIED350345
55.9%
PRIVATE104670
 
16.7%
USFS63043
 
10.1%
BIA35396
 
5.6%
STATE OR PRIVATE24009
 
3.8%
BLM21074
 
3.4%
STATE10485
 
1.7%
NPS5731
 
0.9%
FWS4116
 
0.7%
TRIBAL2948
 
0.5%
Other values (6)5005
 
0.8%

Length

2022-11-23T17:22:46.501631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
missing/not350345
34.1%
specified350345
34.1%
private128679
 
12.5%
usfs63043
 
6.1%
bia35396
 
3.4%
state34494
 
3.4%
or24009
 
2.3%
blm21074
 
2.0%
nps5731
 
0.6%
fws4116
 
0.4%
Other values (8)10827
 
1.1%

Most occurring characters

ValueCountFrequency (%)
I1572016
17.3%
S1221462
13.5%
E873258
9.6%
N709982
 
7.8%
T553668
 
6.1%
P486175
 
5.4%
F421151
 
4.6%
401237
 
4.4%
O378592
 
4.2%
M372839
 
4.1%
Other values (13)2070625
22.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter8308003
91.7%
Space Separator401237
 
4.4%
Other Punctuation351765
 
3.9%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I1572016
18.9%
S1221462
14.7%
E873258
10.5%
N709982
8.5%
T553668
 
6.7%
P486175
 
5.9%
F421151
 
5.1%
O378592
 
4.6%
M372839
 
4.5%
D354753
 
4.3%
Other values (11)1364107
16.4%
Space Separator
ValueCountFrequency (%)
401237
100.0%
Other Punctuation
ValueCountFrequency (%)
/351765
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8308003
91.7%
Common753002
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
I1572016
18.9%
S1221462
14.7%
E873258
10.5%
N709982
8.5%
T553668
 
6.7%
P486175
 
5.9%
F421151
 
5.1%
O378592
 
4.6%
M372839
 
4.5%
D354753
 
4.3%
Other values (11)1364107
16.4%
Common
ValueCountFrequency (%)
401237
53.3%
/351765
46.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII9061005
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I1572016
17.3%
S1221462
13.5%
E873258
9.6%
N709982
 
7.8%
T553668
 
6.1%
P486175
 
5.4%
F421151
 
4.6%
401237
 
4.4%
O378592
 
4.2%
M372839
 
4.1%
Other values (13)2070625
22.9%

STATE
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.8 MiB
CA
63286 
GA
56261 
TX
47456 
NC
 
37085
FL
 
30148
Other values (47)
392586 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1253644
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowID
2nd rowMT
3rd rowNE
4th rowVA
5th rowLA

Common Values

ValueCountFrequency (%)
CA63286
 
10.1%
GA56261
 
9.0%
TX47456
 
7.6%
NC37085
 
5.9%
FL30148
 
4.8%
SC26909
 
4.3%
NY26833
 
4.3%
MS26235
 
4.2%
AZ23909
 
3.8%
AL22352
 
3.6%
Other values (42)266348
42.5%

Length

2022-11-23T17:22:46.553666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca63286
 
10.1%
ga56261
 
9.0%
tx47456
 
7.6%
nc37085
 
5.9%
fl30148
 
4.8%
sc26909
 
4.3%
ny26833
 
4.3%
ms26235
 
4.2%
az23909
 
3.8%
al22352
 
3.6%
Other values (42)266348
42.5%

Most occurring characters

ValueCountFrequency (%)
A213896
17.1%
C140245
11.2%
N125281
 
10.0%
T83507
 
6.7%
M83351
 
6.6%
S66006
 
5.3%
L63050
 
5.0%
G56261
 
4.5%
O53481
 
4.3%
X47456
 
3.8%
Other values (14)321110
25.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter1253644
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A213896
17.1%
C140245
11.2%
N125281
 
10.0%
T83507
 
6.7%
M83351
 
6.6%
S66006
 
5.3%
L63050
 
5.0%
G56261
 
4.5%
O53481
 
4.3%
X47456
 
3.8%
Other values (14)321110
25.6%

Most occurring scripts

ValueCountFrequency (%)
Latin1253644
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A213896
17.1%
C140245
11.2%
N125281
 
10.0%
T83507
 
6.7%
M83351
 
6.6%
S66006
 
5.3%
L63050
 
5.0%
G56261
 
4.5%
O53481
 
4.3%
X47456
 
3.8%
Other values (14)321110
25.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1253644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A213896
17.1%
C140245
11.2%
N125281
 
10.0%
T83507
 
6.7%
M83351
 
6.6%
S66006
 
5.3%
L63050
 
5.0%
G56261
 
4.5%
O53481
 
4.3%
X47456
 
3.8%
Other values (14)321110
25.6%

COUNTY
Categorical

HIGH CARDINALITY
MISSING

Distinct3090
Distinct (%)0.8%
Missing226115
Missing (%)36.1%
Memory size4.8 MiB
5
 
2534
SUFFOLK
 
2482
Lincoln
 
2451
Polk
 
2315
Washington
 
2300
Other values (3085)
388625 

Length

Max length50
Median length19
Mean length7.124013306
Min length1

Characters and Unicode

Total characters2854642
Distinct characters72
Distinct categories9 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique320 ?
Unique (%)0.1%

Sample

1st rowKootenai
2nd rowRockcastle
3rd rowBARRY
4th rowMille Lacs
5th row31

Common Values

ValueCountFrequency (%)
52534
 
0.4%
SUFFOLK2482
 
0.4%
Lincoln2451
 
0.4%
Polk2315
 
0.4%
Washington2300
 
0.4%
Cherokee2291
 
0.4%
Oahu2278
 
0.4%
Marion2229
 
0.4%
Jackson2129
 
0.3%
Lee1915
 
0.3%
Other values (3080)377783
60.3%
(Missing)226115
36.1%

Length

2022-11-23T17:22:46.604440image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
county7734
 
1.8%
san3482
 
0.8%
washington3391
 
0.8%
st3378
 
0.8%
lincoln2797
 
0.6%
jefferson2734
 
0.6%
marion2613
 
0.6%
cherokee2605
 
0.6%
polk2593
 
0.6%
suffolk2571
 
0.6%
Other values (2037)398883
92.2%

Most occurring characters

ValueCountFrequency (%)
310150
 
10.9%
a205995
 
7.2%
e194606
 
6.8%
n166450
 
5.8%
o165547
 
5.8%
r138590
 
4.9%
l116812
 
4.1%
i108649
 
3.8%
t97037
 
3.4%
s91182
 
3.2%
Other values (62)1259624
44.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1726080
60.5%
Uppercase Letter709306
24.8%
Space Separator310150
 
10.9%
Decimal Number106488
 
3.7%
Other Punctuation2422
 
0.1%
Dash Punctuation185
 
< 0.1%
Connector Punctuation7
 
< 0.1%
Open Punctuation2
 
< 0.1%
Close Punctuation2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a205995
11.9%
e194606
11.3%
n166450
9.6%
o165547
9.6%
r138590
 
8.0%
l116812
 
6.8%
i108649
 
6.3%
t97037
 
5.6%
s91182
 
5.3%
u63211
 
3.7%
Other values (16)378001
21.9%
Uppercase Letter
ValueCountFrequency (%)
C61563
 
8.7%
S53481
 
7.5%
A51610
 
7.3%
E49648
 
7.0%
L44280
 
6.2%
O42397
 
6.0%
R42044
 
5.9%
N41404
 
5.8%
M39616
 
5.6%
B31116
 
4.4%
Other values (16)252147
35.5%
Decimal Number
ValueCountFrequency (%)
118070
17.0%
316425
15.4%
013656
12.8%
511506
10.8%
910957
10.3%
710099
9.5%
29299
8.7%
46724
 
6.3%
65041
 
4.7%
84711
 
4.4%
Other Punctuation
ValueCountFrequency (%)
.2205
91.0%
&104
 
4.3%
'97
 
4.0%
,9
 
0.4%
/7
 
0.3%
Space Separator
ValueCountFrequency (%)
310150
100.0%
Dash Punctuation
ValueCountFrequency (%)
-185
100.0%
Connector Punctuation
ValueCountFrequency (%)
_7
100.0%
Open Punctuation
ValueCountFrequency (%)
(2
100.0%
Close Punctuation
ValueCountFrequency (%)
)2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2435386
85.3%
Common419256
 
14.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a205995
 
8.5%
e194606
 
8.0%
n166450
 
6.8%
o165547
 
6.8%
r138590
 
5.7%
l116812
 
4.8%
i108649
 
4.5%
t97037
 
4.0%
s91182
 
3.7%
u63211
 
2.6%
Other values (42)1087307
44.6%
Common
ValueCountFrequency (%)
310150
74.0%
118070
 
4.3%
316425
 
3.9%
013656
 
3.3%
511506
 
2.7%
910957
 
2.6%
710099
 
2.4%
29299
 
2.2%
46724
 
1.6%
65041
 
1.2%
Other values (10)7329
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2854642
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
310150
 
10.9%
a205995
 
7.2%
e194606
 
6.8%
n166450
 
5.8%
o165547
 
5.8%
r138590
 
4.9%
l116812
 
4.1%
i108649
 
3.8%
t97037
 
3.4%
s91182
 
3.2%
Other values (62)1259624
44.1%

FIPS_CODE
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct282
Distinct (%)0.1%
Missing226115
Missing (%)36.1%
Infinite0
Infinite (%)0.0%
Mean95.71289745
Minimum1
Maximum810
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.8 MiB
2022-11-23T17:22:46.664356image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q129
median67
Q3121
95-th percentile313
Maximum810
Range809
Interquartile range (IQR)92

Descriptive statistics

Standard deviation98.61404754
Coefficient of variation (CV)1.030310963
Kurtosis3.892270859
Mean95.71289745
Median Absolute Deviation (MAD)42
Skewness1.924176714
Sum38352828
Variance9724.730372
MonotonicityNot monotonic
2022-11-23T17:22:46.726277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59676
 
1.5%
39578
 
1.5%
299435
 
1.5%
18933
 
1.4%
77540
 
1.2%
197431
 
1.2%
177199
 
1.1%
157066
 
1.1%
356912
 
1.1%
216481
 
1.0%
Other values (272)320456
51.1%
(Missing)226115
36.1%
ValueCountFrequency (%)
18933
1.4%
39578
1.5%
59676
1.5%
6137
 
< 0.1%
77540
1.2%
95467
0.9%
114572
0.7%
1250
 
< 0.1%
135904
0.9%
157066
1.1%
ValueCountFrequency (%)
8105
 
< 0.1%
80016
 
< 0.1%
7601
 
< 0.1%
7301
 
< 0.1%
7002
 
< 0.1%
6501
 
< 0.1%
5504
 
< 0.1%
5301
 
< 0.1%
51042
< 0.1%
50748
< 0.1%

FIPS_NAME
Categorical

HIGH CARDINALITY
MISSING

Distinct1658
Distinct (%)0.4%
Missing226115
Missing (%)36.1%
Memory size4.8 MiB
Washington
 
3757
Lincoln
 
3493
Jackson
 
3325
Marion
 
2970
Cherokee
 
2878
Other values (1653)
384284 

Length

Max length31
Median length17
Mean length6.994756767
Min length3

Characters and Unicode

Total characters2802848
Distinct characters57
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)< 0.1%

Sample

1st rowKootenai
2nd rowRockcastle
3rd rowBarry
4th rowMille Lacs
5th rowCook

Common Values

ValueCountFrequency (%)
Washington3757
 
0.6%
Lincoln3493
 
0.6%
Jackson3325
 
0.5%
Marion2970
 
0.5%
Cherokee2878
 
0.5%
Polk2769
 
0.4%
Monroe2696
 
0.4%
Coconino2634
 
0.4%
Suffolk2555
 
0.4%
Jefferson2540
 
0.4%
Other values (1648)371090
59.2%
(Missing)226115
36.1%

Length

2022-11-23T17:22:46.788425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san4282
 
1.0%
washington3757
 
0.9%
st3666
 
0.9%
lincoln3493
 
0.8%
jackson3325
 
0.8%
jefferson3132
 
0.7%
marion2970
 
0.7%
cherokee2878
 
0.7%
polk2769
 
0.6%
monroe2696
 
0.6%
Other values (1683)395015
92.3%

Most occurring characters

ValueCountFrequency (%)
a282199
 
10.1%
e270858
 
9.7%
n226991
 
8.1%
o226231
 
8.1%
r188173
 
6.7%
l161870
 
5.8%
i147128
 
5.2%
s127672
 
4.6%
t119668
 
4.3%
u80673
 
2.9%
Other values (47)971385
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2339159
83.5%
Uppercase Letter432042
 
15.4%
Space Separator27276
 
1.0%
Other Punctuation3808
 
0.1%
Dash Punctuation561
 
< 0.1%
Open Punctuation1
 
< 0.1%
Close Punctuation1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a282199
12.1%
e270858
11.6%
n226991
9.7%
o226231
9.7%
r188173
 
8.0%
l161870
 
6.9%
i147128
 
6.3%
s127672
 
5.5%
t119668
 
5.1%
u80673
 
3.4%
Other values (16)507696
21.7%
Uppercase Letter
ValueCountFrequency (%)
C52616
 
12.2%
S38012
 
8.8%
M37657
 
8.7%
L32064
 
7.4%
B30020
 
6.9%
W25569
 
5.9%
H24145
 
5.6%
P23443
 
5.4%
R18025
 
4.2%
D17937
 
4.2%
Other values (15)132554
30.7%
Other Punctuation
ValueCountFrequency (%)
.3670
96.4%
'138
 
3.6%
Space Separator
ValueCountFrequency (%)
27276
100.0%
Dash Punctuation
ValueCountFrequency (%)
-561
100.0%
Open Punctuation
ValueCountFrequency (%)
(1
100.0%
Close Punctuation
ValueCountFrequency (%)
)1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2771201
98.9%
Common31647
 
1.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a282199
 
10.2%
e270858
 
9.8%
n226991
 
8.2%
o226231
 
8.2%
r188173
 
6.8%
l161870
 
5.8%
i147128
 
5.3%
s127672
 
4.6%
t119668
 
4.3%
u80673
 
2.9%
Other values (41)939738
33.9%
Common
ValueCountFrequency (%)
27276
86.2%
.3670
 
11.6%
-561
 
1.8%
'138
 
0.4%
(1
 
< 0.1%
)1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII2802848
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a282199
 
10.1%
e270858
 
9.7%
n226991
 
8.1%
o226231
 
8.1%
r188173
 
6.7%
l161870
 
5.8%
i147128
 
5.2%
s127672
 
4.6%
t119668
 
4.3%
u80673
 
2.9%
Other values (47)971385
34.7%

Shape
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size4.8 MiB

Interactions

2022-11-23T17:22:16.389847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:50.190146image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:58.061054image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:06.026317image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:14.954420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:24.009084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:32.779588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:16.885035image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:25.603234image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:33.629080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:41.874622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:50.543967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:59.118982image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:07.472112image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:17.089622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:50.347071image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:58.202100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:06.167248image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:15.117890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:24.169394image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:35.454571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:17.040598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:25.712970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:33.747218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:42.011153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:50.711484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:59.265135image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:07.621459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:17.775487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:50.498698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:58.343524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:06.308881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:15.275516image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:24.323240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:38.036759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:17.197559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:25.825485image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:33.880472image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:42.164206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:50.859088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:59.416165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:07.767185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:18.637380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:50.644775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:58.484432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:06.450113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:15.417623image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:24.472803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:40.818601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:17.347521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:25.933067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:34.012620image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:42.334145image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:51.006826image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:59.560915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:07.913803image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:19.310461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:50.785938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:58.667243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:06.592059image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:15.563329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:24.619212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:43.330971image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:17.494648image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:26.040556image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:34.139166image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:42.640771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:51.153069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:59.703046image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:08.070731image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:20.015539image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:51.005140image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:58.870286image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:06.796304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:15.793232image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:24.870641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:46.157347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:17.726657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:26.252938image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:34.379906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:42.850470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:51.380073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:59.915422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:08.302007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:26.118012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:56.023218image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:03.956382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:12.214324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:21.525359image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:30.448010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:54.330223image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:23.390538image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:31.716600image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:39.966667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:48.368371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:56.830832image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:05.324001image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:13.985258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:26.956719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:56.132902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:04.050878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:12.341127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:21.652228image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:30.560617image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:57.130111image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:23.506840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:31.821692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:40.079219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:48.474820image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:56.943062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:05.431325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:14.112038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:27.527768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:56.242559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:04.160619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:12.468499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:21.768554image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:30.666798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:00.006295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:23.625922image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:31.929194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:40.186294image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:48.579116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:57.051202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:05.532325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:14.241114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:28.166587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:56.388959image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:04.301684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:12.690479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:21.937354image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:30.818928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:02.700438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:23.792297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:32.036570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:40.300552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:48.715119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:57.201052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:05.675663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:14.413732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:28.865217image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:56.524439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:04.442929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:13.159487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:22.204149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:30.966948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:05.529666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:23.957360image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:32.143358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:40.413633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:48.860019image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:57.350017image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:05.830350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:14.569254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:29.519406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:56.681383image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:04.568605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:13.410062image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:22.357991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:31.109663image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:07.988969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:24.120179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:32.251324image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:40.523315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:49.002675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:57.498323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:05.977154image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:14.721183image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:30.334913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:56.821947image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:04.715991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:13.587994image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:22.524084image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:31.262025image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:10.723914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:24.284413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:32.360370image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:40.633629image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:49.141105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:57.646116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:06.129206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:14.865780image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:31.100698image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:19:57.056844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:04.928841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:13.846729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:22.766415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:20:31.497361image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:13.221531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:24.533460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:32.709435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:40.820005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:49.369701image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:21:57.872901image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:06.359714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-23T17:22:15.093027image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-23T17:22:46.846421image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an easily interpretable pairwise column metric of the following mapping: vartype-vartype : method, categorical-categorical : Cramer's V, numerical-categorical : Cramer's V (using a discretized numerical column), numerical-numerical : Spearman's ρ. This configuration uses the best suitable for each pair of columns.
2022-11-23T17:22:46.952718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-23T17:22:47.038642image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-23T17:22:47.125109image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-23T17:22:47.208618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-23T17:22:47.290120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-23T17:22:33.568380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-23T17:22:35.854580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-11-23T17:22:39.985426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-11-23T17:22:41.756876image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

OBJECTIDFOD_IDFPA_IDSOURCE_SYSTEM_TYPESOURCE_SYSTEMNWCG_REPORTING_AGENCYNWCG_REPORTING_UNIT_IDNWCG_REPORTING_UNIT_NAMESOURCE_REPORTING_UNITSOURCE_REPORTING_UNIT_NAMELOCAL_FIRE_REPORT_IDLOCAL_INCIDENT_IDFIRE_CODEFIRE_NAMEICS_209_INCIDENT_NUMBERICS_209_NAMEMTBS_IDMTBS_FIRE_NAMECOMPLEX_NAMEFIRE_YEARDISCOVERY_DATEDISCOVERY_DOYDISCOVERY_TIMESTAT_CAUSE_CODESTAT_CAUSE_DESCRCONT_DATECONT_DOYCONT_TIMEFIRE_SIZEFIRE_SIZE_CLASSLATITUDELONGITUDEOWNER_CODEOWNER_DESCRSTATECOUNTYFIPS_CODEFIPS_NAMEShape
0463445497959SFO-ID0043_3101998026NONFEDST-NASFST/C&LUSIDIDSIdaho Department of LandsID310IDS District 310NoneNoneNoneNoneNoneNoneNoneNoneNone19982451073.5260None13.0Missing/UndefinedNaNNaNNone2.0B47.600845-116.30678914.0MISSING/NOT SPECIFIEDIDKootenai055Kootenaib'\x00\x01\xad\x10\x00\x00pqTn\xa2\x13]\xc0\x90{,}\xe8\xccG@pqTn\xa2\x13]\xc0\x90{,}\xe8\xccG@|\x01\x00\x00\x00pqTn\xa2\x13]\xc0\x90{,}\xe8\xccG@\xfe'
11464479201104109W-626311FEDDOI-WFMIBIAUSMTFBAFort Belknap AgencyMTFBAFort Belknap Agency42NoneGB2SWEST DODSONNoneNoneNoneNoneNone20112455795.523416459.0Miscellaneous2455795.5234.018000.1A48.403300-108.2896008.0PRIVATEMTNoneNoneNoneb'\x00\x01\xad\x10\x00\x00\xf8:p\xce\x88\x12[\xc0\x10=\x9bU\x9f3H@\xf8:p\xce\x88\x12[\xc0\x10=\x9bU\x9f3H@|\x01\x00\x00\x00\xf8:p\xce\x88\x12[\xc0\x10=\x9bU\x9f3H@\xfe'
21591094201608816SFO-NE-2012-16029NONFEDST-NASFST/C&LUSNENESNebraska Department of ForestryNENFSNebraska Forest ServiceNone16029NoneNoneNoneNoneNoneNoneNone20122456066.514015479.0Miscellaneous2456066.5140.016301.5B40.668460-99.08913014.0MISSING/NOT SPECIFIEDNENoneNoneNoneb'\x00\x01\xad\x10\x00\x00\xe8\xc5PN\xb4\xc5X\xc0\x98W\xe7\x18\x90UD@\xe8\xc5PN\xb4\xc5X\xc0\x98W\xe7\x18\x90UD@|\x01\x00\x00\x00\xe8\xc5PN\xb4\xc5X\xc0\x98W\xe7\x18\x90UD@\xfe'
39447261072277SWRA_VA_11300NONFEDST-VAVASST/C&LUSVAVASVirginia Department of ForestryVAVAS3Farmville DistrictNone9801NoneNoneNoneNoneNoneNoneNone20022452278.54None9.0MiscellaneousNaNNaNNone2.0B39.000000-78.26920014.0MISSING/NOT SPECIFIEDVANoneNoneNoneb'\x00\x01\xad\x10\x00\x00P\x05\xa3\x92:\x91S\xc0\x08\x00\x00\x00\x00\x80C@P\x05\xa3\x92:\x91S\xc0\x08\x00\x00\x00\x00\x80C@|\x01\x00\x00\x00P\x05\xa3\x92:\x91S\xc0\x08\x00\x00\x00\x00\x80C@\xfe'
48962771022153SWRA_LA_24392NONFEDST-LALASST/C&LUSLALASLouisiana Office of ForestryLALAS1LAS District 1NoneLA1-12NoneNoneNoneNoneNoneNoneNone20022452410.5136None7.0ArsonNaNNaNNone5.0B30.993300-89.86390014.0MISSING/NOT SPECIFIEDLANoneNoneNoneb'\x00\x01\xad\x10\x00\x00\xe8\xc09#JwV\xc0\xf0\x1d\xa7\xe8H\xfe>@\xe8\xc09#JwV\xc0\xf0\x1d\xa7\xe8H\xfe>@|\x01\x00\x00\x00\xe8\xc09#JwV\xc0\xf0\x1d\xa7\xe8H\xfe>@\xfe'
512690391673620SFO-KY-20320089011NONFEDST-NASFST/C&LUSKYKYSKentucky Division of ForestryKYKYSKentucky Division of ForestryNone20320089011NoneNoneNoneNoneNoneNoneNone20082454574.510920455.0Debris Burning2454574.5109.020453.0B37.269210-84.34258014.0MISSING/NOT SPECIFIEDKYRockcastle203Rockcastleb'\x00\x01\xad\x10\x00\x00\xdc\x10\xaa\xd4\xec\x15U\xc0\xd0\xe0(yu\xa2B@\xdc\x10\xaa\xd4\xec\x15U\xc0\xd0\xe0(yu\xa2B@|\x01\x00\x00\x00\xdc\x10\xaa\xd4\xec\x15U\xc0\xd0\xe0(yu\xa2B@\xfe'
6145384520009452MOSFM-11020NONFEDST-MOMOSST/C&LUSMOMOSMissouri Department of Conservation00506BUTTERFIELD FDNoneNoneNoneNoneNoneNoneNoneNoneNone19992451502.532416433.0SmokingNaNNaNNone10.0C39.654400-94.9267108.0PRIVATEMOBARRY009Barryb'\x00\x01\xad\x10\x00\x00\x10\xb8u7O\xbbW\xc0X@\x13a\xc3\xd3C@\x10\xb8u7O\xbbW\xc0X@\x13a\xc3\xd3C@|\x01\x00\x00\x00\x10\xb8u7O\xbbW\xc0X@\x13a\xc3\xd3C@\xfe'
7832173953407STATE_MS_93764NONFEDST-MSMSSST/C&LUSMSMSSMississippi Forestry CommissionMS South CentralMSS South Central DistrictNone01-007NoneNoneNoneNoneNoneNoneNone20012452032.512317407.0ArsonNaNNaNNone10.0C31.437500-89.27560014.0MISSING/NOT SPECIFIEDMSNoneNoneNoneb'\x00\x01\xad\x10\x00\x00\xc0\xb1.n\xa3QV\xc0\x10\x00\x00\x00\x00p?@\xc0\xb1.n\xa3QV\xc0\x10\x00\x00\x00\x00p?@|\x01\x00\x00\x00\xc0\xb1.n\xa3QV\xc0\x10\x00\x00\x00\x00p?@\xfe'
8500074537645SFO-MN0349-920122NONFEDST-NASFST/C&LUSMNMNSMinnesota Department of Natural ResourcesMNMNSMinnesota Department of Natural ResourcesNoneNoneNoneNoneNoneNoneNoneNoneNone19962450375.5293None4.0CampfireNaNNaNNone70.0C45.969984-93.56845214.0MISSING/NOT SPECIFIEDMNMille Lacs095Mille Lacsb'\x00\x01\xad\x10\x00\x00Hj\x91\x82adW\xc0\xe0\xbf\x1dn(\xfcF@Hj\x91\x82adW\xc0\xe0\xbf\x1dn(\xfcF@|\x01\x00\x00\x00Hj\x91\x82adW\xc0\xe0\xbf\x1dn(\xfcF@\xfe'
953325356FS-1429214FEDFS-FIRESTATFSUSMNSUFSuperior National Forest0909Superior National Forest25TOF-03B2JNTIMBER LAKENoneNoneNoneNoneNone20052453580.521016004.0Campfire2453581.5211.017250.1A47.747778-90.9991675.0USFSMN31031Cookb'\x00\x01\xad\x10\x00\x00\xcc\xb9\xc2X\xf2\xbfV\xc0H\xe3\xaa.\xb7\xdfG@\xcc\xb9\xc2X\xf2\xbfV\xc0H\xe3\xaa.\xb7\xdfG@|\x01\x00\x00\x00\xcc\xb9\xc2X\xf2\xbfV\xc0H\xe3\xaa.\xb7\xdfG@\xfe'

Last rows

OBJECTIDFOD_IDFPA_IDSOURCE_SYSTEM_TYPESOURCE_SYSTEMNWCG_REPORTING_AGENCYNWCG_REPORTING_UNIT_IDNWCG_REPORTING_UNIT_NAMESOURCE_REPORTING_UNITSOURCE_REPORTING_UNIT_NAMELOCAL_FIRE_REPORT_IDLOCAL_INCIDENT_IDFIRE_CODEFIRE_NAMEICS_209_INCIDENT_NUMBERICS_209_NAMEMTBS_IDMTBS_FIRE_NAMECOMPLEX_NAMEFIRE_YEARDISCOVERY_DATEDISCOVERY_DOYDISCOVERY_TIMESTAT_CAUSE_CODESTAT_CAUSE_DESCRCONT_DATECONT_DOYCONT_TIMEFIRE_SIZEFIRE_SIZE_CLASSLATITUDELONGITUDEOWNER_CODEOWNER_DESCRSTATECOUNTYFIPS_CODEFIPS_NAMEShape
626812134626135959FS-361568FEDFS-FIRESTATFSUSSDBKFBlack Hills National Forest0203Black Hills National Forest176None0708POTATO IVNoneNoneNoneNoneNone20002451789.524619304.0Campfire2451789.5246.020070.10A43.961667-103.8466675.0USFSSDNoneNoneNoneb'\x00\x01\xad\x10\x00\x00\xd8\x90f\xc9/\xf6Y\xc0\xd8\xa6\xb8\xe4\x17\xfbE@\xd8\x90f\xc9/\xf6Y\xc0\xd8\xa6\xb8\xe4\x17\xfbE@|\x01\x00\x00\x00\xd8\x90f\xc9/\xf6Y\xc0\xd8\xa6\xb8\xe4\x17\xfbE@\xfe'
626813192970195596W-354694FEDDOI-WFMIBIAUSMTBFABlackfeet AgencyMTBFABlackfeet Agency83NoneNoneRAILROADNoneNoneNoneNoneNone19982451045.523218006.0Railroad2451045.5232.018451.00B48.583300-112.9176002.0BIAMTNoneNoneNoneb'\x00\x01\xad\x10\x00\x00\xd0\xb3Y\xf5\xb9:\\\xc0\xe8\xe0\x0b\x93\xa9JH@\xd0\xb3Y\xf5\xb9:\\\xc0\xe8\xe0\x0b\x93\xa9JH@|\x01\x00\x00\x00\xd0\xb3Y\xf5\xb9:\\\xc0\xe8\xe0\x0b\x93\xa9JH@\xfe'
62681412258341605538SFO-MS-2008-MS3953313193NONFEDST-NASFST/C&LUSMSMSSMississippi Forestry CommissionMFCMississippi Forestry CommissionNoneMS3953313193NoneMS0 0808-3310326003NoneNoneNoneNoneNone20082454551.586113313.0Missing/UndefinedNaNNaNNone5.00B31.862968-89.12884614.0MISSING/NOT SPECIFIEDMSJasper061Jasperb'\x00\x01\xad\x10\x00\x00\x18\x0eK\x03?HV\xc0\xa0~\x89x\xeb\xdc?@\x18\x0eK\x03?HV\xc0\xa0~\x89x\xeb\xdc?@|\x01\x00\x00\x00\x18\x0eK\x03?HV\xc0\xa0~\x89x\xeb\xdc?@\xfe'
62681512318871625902SFO-CO-2010-64229NONFEDST-NASFST/C&LUSCOCOSColorado State Forest Service-State HeadquartersCOSFSColorado State Forest ServiceNone64229NoneNoneNoneNoneNoneNoneNone20102455280.58416389.0Miscellaneous2455280.584.0163820.00C38.818600-107.74800014.0MISSING/NOT SPECIFIEDCODELTA029Deltab'\x00\x01\xad\x10\x00\x00\x1cZd;\xdf\xefZ\xc0\xc0@\x82\xe2\xc7hC@\x1cZd;\xdf\xefZ\xc0\xc0@\x82\xe2\xc7hC@|\x01\x00\x00\x00\x1cZd;\xdf\xefZ\xc0\xc0@\x82\xe2\xc7hC@\xfe'
6268161730993300013912W-676283FEDDOI-WFMIBLMUSIDBODBoise DistrictIDBPDBirds of Prey Field OfficeNoneNoneJHQ1OTA 139NoneNoneNoneNoneNone20142456889.523213502.0Equipment Use2456889.5232.013550.30B43.252190-116.1680101.0BLMIDAda001Adab'\x00\x01\xad\x10\x00\x00\xa8\xd9\x03\xad\xc0\n]\xc0p0\r\xc3G\xa0E@\xa8\xd9\x03\xad\xc0\n]\xc0p0\r\xc3G\xa0E@|\x01\x00\x00\x00\xa8\xd9\x03\xad\xc0\n]\xc0p0\r\xc3G\xa0E@\xfe'
62681713432721822288SFO-NY-NY5802-2005-0000067NONFEDST-NASFST/C&LUSNYNYXFire Department of New YorkNY5802Fire Department of New YorkNoneNY5802-2005-0000067NoneNoneNoneNoneNoneNoneNone20052453519.514923004.0Campfire2453519.5149.023000.28B43.032319-73.40302914.0MISSING/NOT SPECIFIEDNYWASHINGTON115Washingtonb'\x00\x01\xad\x10\x00\x00\xa8.};\xcbYR\xc0\x80\xb00\n#\x84E@\xa8.};\xcbYR\xc0\x80\xb00\n#\x84E@|\x01\x00\x00\x00\xa8.};\xcbYR\xc0\x80\xb00\n#\x84E@\xfe'
62681812443271647454SFO-GA-FY2001-Cook-001NONFEDST-NASFST/C&LUSGAGASGeorgia Forestry CommissionGAGASGeorgia Forestry CommissionNoneFY2001-Cook-001NoneFY2001-COOK-001NoneNoneNoneNoneNone20002451737.519412231.0Lightning2451737.5194.012230.01A31.052800-83.2971008.0PRIVATEGACook075Cookb'\x00\x01\xad\x10\x00\x00\x0c\xe9\xb7\xaf\x03\xd3T\xc0\xa0:\x01M\x84\r?@\x0c\xe9\xb7\xaf\x03\xd3T\xc0\xa0:\x01M\x84\r?@|\x01\x00\x00\x00\x0c\xe9\xb7\xaf\x03\xd3T\xc0\xa0:\x01M\x84\r?@\xfe'
62681912482921651452SFO-GA-FY2001-Polk-071NONFEDST-NASFST/C&LUSGAGASGeorgia Forestry CommissionGAGASGeorgia Forestry CommissionNoneFY2001-Polk-071NoneFY2001-POLK-071NoneNoneNoneNoneNone20012452032.512312356.0Railroad2452032.5123.012350.80B33.984400-85.00310014.0MISSING/NOT SPECIFIEDGAPolk233Polkb'\x00\x01\xad\x10\x00\x00\x84\xa7W\xca2@U\xc0`\x17\xb7\xd1\x00\xfe@@\x84\xa7W\xca2@U\xc0`\x17\xb7\xd1\x00\xfe@@|\x01\x00\x00\x00\x84\xa7W\xca2@U\xc0`\x17\xb7\xd1\x00\xfe@@\xfe'
62682010917551329972CDF_2007_53_2209_005972NONFEDST-CACDFST/C&LUSCACZUSan Mateo-Santa Cruz UnitCACZUSan Mateo-Santa Cruz UnitNone005972NoneBRANCIFORTE DR SOQUEL 5NoneNoneNoneNoneNone20072454294.5194None2.0Equipment UseNaNNaNNone0.30B37.048889-121.98194414.0MISSING/NOT SPECIFIEDCANoneNoneNoneb'\x00\x01\xad\x10\x00\x00|\x12~-\xd8~^\xc0\x80^\xa6\xfdA\x86B@|\x12~-\xd8~^\xc0\x80^\xa6\xfdA\x86B@|\x01\x00\x00\x00|\x12~-\xd8~^\xc0\x80^\xa6\xfdA\x86B@\xfe'
6268219275041054909SWRA_SC_68541NONFEDST-SCSCSST/C&LUSSCSCSSouth Carolina Forestry CommissionSCSCS3SCS Unit 3NoneP193NoneNoneNoneNoneNoneNoneNone20022452358.584None5.0Debris BurningNaNNaNNone15.00C33.816700-80.33330014.0MISSING/NOT SPECIFIEDSCNoneNoneNoneb"\x00\x01\xad\x10\x00\x00l\xf0\x85\xc9T\x15T\xc0XR'\xa0\x89\xe8@@l\xf0\x85\xc9T\x15T\xc0XR'\xa0\x89\xe8@@|\x01\x00\x00\x00l\xf0\x85\xc9T\x15T\xc0XR'\xa0\x89\xe8@@\xfe"